{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimizer Benchmark\n",
    "\n",
    "This notebook compares different optimization solvers from `cadre` on the Rosenbrock function.\n",
    "\n",
    "## Test Matrix\n",
    "\n",
    "| Solver Category | Configurations | Total Runs |\n",
    "|-----------------|----------------|------------|\n",
    "| Adam/SGD/Ada | 4 solvers x 2 lr x 2 precond | 16 |\n",
    "| Active Set v1 | 4 solvers x 3 lr x 2 linesearch | 24 |\n",
    "| Active Set v2 | 4 solvers x 3 lr x 2 linesearch | 24 |\n",
    "| LBFGS | 2 linesearch x 2 precond | 4 |\n",
    "| **Total** | | **68** |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-16T10:05:04.738504Z",
     "iopub.status.busy": "2026-01-16T10:05:04.738259Z",
     "iopub.status.idle": "2026-01-16T10:05:06.526028Z",
     "shell.execute_reply": "2026-01-16T10:05:06.525350Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0123 17:10:34.574353   31328 cuda_executor.cc:1802] GPU interconnect information not available: INTERNAL: NVML doesn't support extracting fabric info or NVLink is not used by the device.\n",
      "W0123 17:10:34.581748   31253 cuda_executor.cc:1802] GPU interconnect information not available: INTERNAL: NVML doesn't support extracting fabric info or NVLink is not used by the device.\n"
     ]
    }
   ],
   "source": [
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from cadre import minimize\n",
    "\n",
    "# Enable 64-bit precision\n",
    "jax.config.update(\"jax_enable_x64\", True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-16T10:05:06.528172Z",
     "iopub.status.busy": "2026-01-16T10:05:06.527983Z",
     "iopub.status.idle": "2026-01-16T10:05:06.827647Z",
     "shell.execute_reply": "2026-01-16T10:05:06.827265Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f(x*) = 7.29e+06\n",
      "f(x0) = 2.06e+03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100.00%|██████████| [00:00<00:00, 1994.38%/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimized f(x) = 1.18e+03\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Smallest eigenvalue: 17.231431250435254\n",
      "Associated vector: [-5.87074112e-05  1.32507283e-04  4.90897627e-04 -1.21946326e-03\n",
      " -4.58686639e-03  1.14099154e-02  4.29246869e-02 -1.06786199e-01\n",
      " -4.03218167e-01  9.07753443e-01]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def rosenbrock(x):\n",
    "    \"\"\"Rosenbrock function.\n",
    "\n",
    "    f(x) = sum(100*(x[i+1] - x[i]^2)^2 + (1 - x[i])^2)\n",
    "\n",
    "    Minimum: x* = [1, 1, ..., 1], f(x*) = 0\n",
    "    \"\"\"\n",
    "    return jnp.sum(100.0 * (x[1:] - x[:-1] ** 2) ** 2 + (1 - x[:-1]) ** 2)\n",
    "\n",
    "\n",
    "# Test the function\n",
    "n_dims = 10\n",
    "x_opt = jnp.ones(n_dims) * 10\n",
    "print(f\"f(x*) = {rosenbrock(x_opt):.2e}\")\n",
    "\n",
    "# Classic starting point\n",
    "x0 = jnp.array([-1.2, 1.0] * (n_dims // 2))\n",
    "print(f\"f(x0) = {rosenbrock(x0):.2e}\")\n",
    "\n",
    "\n",
    "param, state = minimize(\n",
    "    rosenbrock,\n",
    "    x0,\n",
    "    solver_name=\"adam\",\n",
    "    max_iter=200,\n",
    "    atol=1e-15,\n",
    "    rtol=1e-15,\n",
    "    lower_bound=-5.0,\n",
    "    upper_bound=5.0,\n",
    ")\n",
    "\n",
    "print(f\"Optimized f(x) = {rosenbrock(param):.2e}\")\n",
    "\n",
    "perturb_scale = 0.1\n",
    "steps = jnp.arange(-20, 21, 1) * perturb_scale\n",
    "grid_vals = steps.reshape(-1, 1) + param.reshape(1, -1)\n",
    "\n",
    "vals = jax.vmap(rosenbrock)(grid_vals)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(steps, vals)\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"Perturbation from optimized parameters\")\n",
    "plt.ylabel(\"Rosenbrock function value\")\n",
    "plt.title(\"Rosenbrock Function Landscape around Optimized Parameters\")\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# 2. Materialize the full Hessian Matrix\n",
    "H = jax.hessian(rosenbrock)(param)\n",
    "\n",
    "# 3. Compute Eigenvalues/Vectors\n",
    "# eigh guarantees real eigenvalues for symmetric matrices\n",
    "eigvals, eigvecs = jnp.linalg.eigh(H)\n",
    "\n",
    "# eigvals are sorted ascending.\n",
    "# eigvecs[:, i] is the vector corresponding to eigvals[i]\n",
    "print(\"Smallest eigenvalue:\", eigvals[0])\n",
    "print(\"Associated vector:\", eigvecs[:, 0])\n",
    "\n",
    "\n",
    "# Assuming 'eigvals' comes from jnp.linalg.eigh(H)\n",
    "# Sort them just in case (eigh usually sorts, but good practice)\n",
    "eigvals_sorted = jnp.sort(eigvals)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(eigvals_sorted, \"o-\", markersize=4, linewidth=1)\n",
    "plt.yscale(\"log\")  # CRITICAL: Use log scale to see the tiny values\n",
    "plt.title(f\"Hessian Eigenvalue Spectrum (Condition No: {eigvals_sorted[-1]/eigvals_sorted[0]:.1e})\")\n",
    "plt.ylabel(\"Eigenvalue Magnitude (Curvature)\")\n",
    "plt.xlabel(\"Eigenmode Index\")\n",
    "plt.grid(True, which=\"both\", ls=\"-\", alpha=0.5)\n",
    "\n",
    "# Add a \"Dangerous Flatness\" zone\n",
    "plt.axhspan(1e-10, 1e-4, color=\"red\", alpha=0.1, label=\"Numerical Noise / Degeneracy\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming 'eigvals' comes from jnp.linalg.eigh(H)\n",
    "# Sort them just in case (eigh usually sorts, but good practice)\n",
    "eigvals_sorted = jnp.sort(eigvals)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(eigvals_sorted, \"o-\", markersize=4, linewidth=1)\n",
    "plt.yscale(\"log\")  # CRITICAL: Use log scale to see the tiny values\n",
    "plt.title(f\"Hessian Eigenvalue Spectrum (Condition No: {eigvals_sorted[-1]/eigvals_sorted[0]:.1e})\")\n",
    "plt.ylabel(\"Eigenvalue Magnitude (Curvature)\")\n",
    "plt.xlabel(\"Eigenmode Index\")\n",
    "plt.grid(True, which=\"both\", ls=\"-\", alpha=0.5)\n",
    "\n",
    "# Add a \"Dangerous Flatness\" zone\n",
    "plt.axhspan(1e-10, 1e-4, color=\"red\", alpha=0.1, label=\"Numerical Noise / Degeneracy\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([ 402.,  202.,  202.,  202.,  202.,  202.,  202.,  202.,  202.,\n",
       "       -200.], dtype=float64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hessian = jax.hessian(rosenbrock)(param)\n",
    "hessian @ jnp.ones_like(param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Error interpreting argument to <function hvp at 0x7fbc453bb420> as an abstract array. The problematic value is of type <class 'function'> and was passed to the function at path f.\nThis typically means that a jit-wrapped function was called with a non-array argument, and this argument was not marked as static using the static_argnums or static_argnames parameters of jax.jit.",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[18]\u001b[39m\u001b[32m, line 53\u001b[39m\n\u001b[32m     43\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[32m     44\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mmin_algebraic\u001b[39m\u001b[33m\"\u001b[39m: (evals_small, evecs_small), \u001b[38;5;66;03m# Most negative (or smallest positive)\u001b[39;00m\n\u001b[32m     45\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mmin_magnitude\u001b[39m\u001b[33m\"\u001b[39m: (evals_flat, evecs_flat),   \u001b[38;5;66;03m# Closest to zero\u001b[39;00m\n\u001b[32m     46\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mmax_algebraic\u001b[39m\u001b[33m\"\u001b[39m: (evals_large, evecs_large)  \u001b[38;5;66;03m# Largest positive\u001b[39;00m\n\u001b[32m     47\u001b[39m     }\n\u001b[32m     49\u001b[39m \u001b[38;5;66;03m# --- USAGE EXAMPLE ---\u001b[39;00m\n\u001b[32m     50\u001b[39m \u001b[38;5;66;03m# Assume 'param' is your optimized solution from the previous step\u001b[39;00m\n\u001b[32m     51\u001b[39m \u001b[38;5;66;03m# param = ... (your optimized array)\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m53\u001b[39m results = \u001b[43mget_eigenvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrosenbrock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparam\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m     55\u001b[39m lambda_min = results[\u001b[33m\"\u001b[39m\u001b[33mmin_algebraic\u001b[39m\u001b[33m\"\u001b[39m][\u001b[32m0\u001b[39m][\u001b[32m0\u001b[39m]\n\u001b[32m     56\u001b[39m lambda_flat = results[\u001b[33m\"\u001b[39m\u001b[33mmin_magnitude\u001b[39m\u001b[33m\"\u001b[39m][\u001b[32m0\u001b[39m][\u001b[32m0\u001b[39m] \u001b[38;5;66;03m# Should be same as min if positive definite\u001b[39;00m\n",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[18]\u001b[39m\u001b[32m, line 32\u001b[39m, in \u001b[36mget_eigenvalues\u001b[39m\u001b[34m(func, params, k)\u001b[39m\n\u001b[32m     29\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m np.array(hvp(func, params, v_jax))\n\u001b[32m     31\u001b[39m \u001b[38;5;66;03m# Create the LinearOperator\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m32\u001b[39m A = \u001b[43mLinearOperator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mN\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mN\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmatvec\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmatvec\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     34\u001b[39m \u001b[38;5;66;03m# Compute Smallest Eigenvalue (Algebraic) -> Checks for negative curvature\u001b[39;00m\n\u001b[32m     35\u001b[39m evals_small, evecs_small = eigsh(A, k=k, which=\u001b[33m'\u001b[39m\u001b[33mSA\u001b[39m\u001b[33m'\u001b[39m, tol=\u001b[32m1e-5\u001b[39m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/fg/lib/python3.11/site-packages/scipy/sparse/linalg/_interface.py:607\u001b[39m, in \u001b[36m_CustomLinearOperator.__init__\u001b[39m\u001b[34m(self, shape, matvec, rmatvec, matmat, dtype, rmatmat)\u001b[39m\n\u001b[32m    604\u001b[39m \u001b[38;5;28mself\u001b[39m.__rmatmat_impl = rmatmat\n\u001b[32m    605\u001b[39m \u001b[38;5;28mself\u001b[39m.__matmat_impl = matmat\n\u001b[32m--> \u001b[39m\u001b[32m607\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_init_dtype\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/fg/lib/python3.11/site-packages/scipy/sparse/linalg/_interface.py:199\u001b[39m, in \u001b[36mLinearOperator._init_dtype\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    197\u001b[39m v = np.zeros(\u001b[38;5;28mself\u001b[39m.shape[-\u001b[32m1\u001b[39m], dtype=np.int8)\n\u001b[32m    198\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m199\u001b[39m     matvec_v = np.asarray(\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmatvec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m    200\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOverflowError\u001b[39;00m:\n\u001b[32m    201\u001b[39m     \u001b[38;5;66;03m# Python large `int` promoted to `np.int64`or `np.int32`\u001b[39;00m\n\u001b[32m    202\u001b[39m     \u001b[38;5;28mself\u001b[39m.dtype = np.dtype(\u001b[38;5;28mint\u001b[39m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/fg/lib/python3.11/site-packages/scipy/sparse/linalg/_interface.py:258\u001b[39m, in \u001b[36mLinearOperator.matvec\u001b[39m\u001b[34m(self, x)\u001b[39m\n\u001b[32m    255\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m x.shape != (N,) \u001b[38;5;129;01mand\u001b[39;00m x.shape != (N,\u001b[32m1\u001b[39m):\n\u001b[32m    256\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m'\u001b[39m\u001b[33mdimension mismatch\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m258\u001b[39m y = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_matvec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    260\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, np.matrix):\n\u001b[32m    261\u001b[39m     y = asmatrix(y)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/fg/lib/python3.11/site-packages/scipy/sparse/linalg/_interface.py:616\u001b[39m, in \u001b[36m_CustomLinearOperator._matvec\u001b[39m\u001b[34m(self, x)\u001b[39m\n\u001b[32m    615\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_matvec\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[32m--> \u001b[39m\u001b[32m616\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m__matvec_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[18]\u001b[39m\u001b[32m, line 29\u001b[39m, in \u001b[36mget_eigenvalues.<locals>.matvec\u001b[39m\u001b[34m(v)\u001b[39m\n\u001b[32m     27\u001b[39m v_jax = jnp.array(v)\n\u001b[32m     28\u001b[39m \u001b[38;5;66;03m# hvp requires the function, the point (params), and the vector (v)\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m29\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m np.array(\u001b[43mhvp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv_jax\u001b[49m\u001b[43m)\u001b[49m)\n",
      "    \u001b[31m[... skipping hidden 5 frame]\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/micromamba/envs/fg/lib/python3.11/site-packages/jax/_src/pjit.py:678\u001b[39m, in \u001b[36m_infer_input_type\u001b[39m\u001b[34m(fun, dbg_fn, explicit_args)\u001b[39m\n\u001b[32m    676\u001b[39m   dbg = dbg_fn()\n\u001b[32m    677\u001b[39m   arg_description = \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mpath \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdbg.arg_names[i]\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mif\u001b[39;00m\u001b[38;5;250m \u001b[39mdbg.arg_names\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01mis\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01mnot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01melse\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[33m'\u001b[39m\u001b[33munknown\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m  \u001b[38;5;66;03m# pytype: disable=name-error\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m678\u001b[39m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m    679\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mError interpreting argument to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfun\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m as an abstract array.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    680\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m The problematic value is of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(x)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m and was passed to\u001b[39m\u001b[33m\"\u001b[39m  \u001b[38;5;66;03m# pytype: disable=name-error\u001b[39;00m\n\u001b[32m    681\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m the function at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00marg_description\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    682\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mThis typically means that a jit-wrapped function was called with a non-array\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    683\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33m argument, and this argument was not marked as static using the\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    684\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33m static_argnums or static_argnames parameters of jax.jit.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    685\u001b[39m   ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    686\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m config.mutable_array_checks.value:\n\u001b[32m    687\u001b[39m   check_no_aliased_ref_args(dbg_fn, avals, explicit_args)\n",
      "\u001b[31mTypeError\u001b[39m: Error interpreting argument to <function hvp at 0x7fbc453bb420> as an abstract array. The problematic value is of type <class 'function'> and was passed to the function at path f.\nThis typically means that a jit-wrapped function was called with a non-array argument, and this argument was not marked as static using the static_argnums or static_argnames parameters of jax.jit."
     ]
    }
   ],
   "source": [
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import numpy as np\n",
    "from scipy.sparse.linalg import LinearOperator, eigsh\n",
    "\n",
    "\n",
    "# 1. Define your Function (Rosenbrock)\n",
    "def rosenbrock(x):\n",
    "    return jnp.sum(100.0 * (x[1:] - x[:-1] ** 2) ** 2 + (1 - x[:-1]) ** 2)\n",
    "\n",
    "\n",
    "# 2. Define the Hessian-Vector Product (HVP)\n",
    "# This computes H*v using forward-over-reverse auto-diff\n",
    "@jax.jit\n",
    "def hvp(f, primals, tangents):\n",
    "    return jax.jvp(jax.grad(f), (primals,), (tangents,))[1]\n",
    "\n",
    "\n",
    "# 3. Wrapper for SciPy's LinearOperator\n",
    "def get_eigenvalues(func, params, k=1):\n",
    "    \"\"\"\n",
    "    Computes the k smallest and k largest eigenvalues of the Hessian\n",
    "    at 'params' without materializing the matrix.\n",
    "    \"\"\"\n",
    "    N = params.shape[0]\n",
    "\n",
    "    # Define the matrix-vector multiplication for SciPy\n",
    "    def matvec(v):\n",
    "        v_jax = jnp.array(v)\n",
    "        # hvp requires the function, the point (params), and the vector (v)\n",
    "        return np.array(hvp(func, params, v_jax))\n",
    "\n",
    "    # Create the LinearOperator\n",
    "    A = LinearOperator((N, N), matvec=matvec)\n",
    "\n",
    "    # Compute Smallest Eigenvalue (Algebraic) -> Checks for negative curvature\n",
    "    evals_small, evecs_small = eigsh(A, k=k, which=\"SA\", tol=1e-5)\n",
    "\n",
    "    # Compute Smallest Magnitude -> Checks for flatness/degeneracy\n",
    "    evals_flat, evecs_flat = eigsh(A, k=k, which=\"SM\", tol=1e-5)\n",
    "\n",
    "    # Compute Largest Eigenvalue -> Used for Condition Number\n",
    "    evals_large, evecs_large = eigsh(A, k=k, which=\"LA\", tol=1e-5)\n",
    "\n",
    "    return {\n",
    "        \"min_algebraic\": (evals_small, evecs_small),  # Most negative (or smallest positive)\n",
    "        \"min_magnitude\": (evals_flat, evecs_flat),  # Closest to zero\n",
    "        \"max_algebraic\": (evals_large, evecs_large),  # Largest positive\n",
    "    }\n",
    "\n",
    "\n",
    "# --- USAGE EXAMPLE ---\n",
    "# Assume 'param' is your optimized solution from the previous step\n",
    "# param = ... (your optimized array)\n",
    "\n",
    "results = get_eigenvalues(rosenbrock, param, k=1)\n",
    "\n",
    "lambda_min = results[\"min_algebraic\"][0][0]\n",
    "lambda_flat = results[\"min_magnitude\"][0][0]  # Should be same as min if positive definite\n",
    "lambda_max = results[\"max_algebraic\"][0][0]\n",
    "\n",
    "print(f\"Smallest Eigenvalue: {lambda_min:.4e}\")\n",
    "print(f\"Largest Eigenvalue:  {lambda_max:.4e}\")\n",
    "print(f\"Condition Number:    {abs(lambda_max / lambda_flat):.4e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-16T10:05:06.846700Z",
     "iopub.status.busy": "2026-01-16T10:05:06.846556Z",
     "iopub.status.idle": "2026-01-16T10:05:06.851798Z",
     "shell.execute_reply": "2026-01-16T10:05:06.851035Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total configurations: 70\n"
     ]
    }
   ],
   "source": [
    "# Define all benchmark configurations\n",
    "configs = []\n",
    "\n",
    "# Adam/SGD/Ada (16 runs): 4 solvers x 2 lr x 2 precond\n",
    "# for solver in [\"adam\", \"sgd\", \"adabelief\", \"adaw\"]:\n",
    "#    for lr in [1e-3, 1e-2]:\n",
    "#        for precond in [False, True]:\n",
    "#            configs.append({\n",
    "#                \"name\": f\"{solver}_lr{lr}_{'cond' if precond else 'nocond'}\",\n",
    "#                \"solver\": solver,\n",
    "#                \"lr\": lr,\n",
    "#                \"precond\": precond,\n",
    "#                \"category\": \"Adam/SGD\"\n",
    "#            })\n",
    "#\n",
    "# Active Set v1 (24 runs): 4 solvers x 3 lr x 2 linesearch\n",
    "for solver in [\"active_set\", \"active_set_sgd\", \"active_set_adabelief\", \"active_set_adaw\"]:\n",
    "    for lr in [1e-3, 1e-2, 1.0]:\n",
    "        for ls in [\"backtracking\", \"zoom\"]:\n",
    "            configs.append(\n",
    "                {\n",
    "                    \"name\": f\"{solver}_lr{lr}_{ls[:2]}\",\n",
    "                    \"solver\": solver,\n",
    "                    \"lr\": lr,\n",
    "                    \"ls\": ls,\n",
    "                    \"precond\": False,\n",
    "                    \"category\": \"Active Set v1\",\n",
    "                }\n",
    "            )\n",
    "\n",
    "# Active Set v2 (24 runs): 4 solvers x 3 lr x 2 linesearch\n",
    "# for solver in [\"active_set_v2\", \"active_set_v2_sgd\", \"active_set_v2_adabelief\", \"active_set_v2_adaw\"]:\n",
    "#    for lr in [1e-3, 1e-2, 1.0]:\n",
    "#        for ls in [\"backtracking\", \"zoom\"]:\n",
    "#            configs.append({\n",
    "#                \"name\": f\"{solver}_lr{lr}_{ls[:2]}\",\n",
    "#                \"solver\": solver,\n",
    "#                \"lr\": lr,\n",
    "#                \"ls\": ls,\n",
    "#                \"precond\": False,\n",
    "#                \"category\": \"Active Set v2\"\n",
    "#            })\n",
    "\n",
    "# LBFGS (4 runs): 2 linesearch x 2 precond\n",
    "# for ls_type, solver in [(\"backtracking\", \"optax_lbfgs\"), (\"zoom\", \"optax_lbfgs\")]:\n",
    "#    for precond in [False, True]:\n",
    "#        configs.append({\n",
    "#            \"name\": f\"lbfgs_{ls_type[:2]}_{'cond' if precond else 'nocond'}\",\n",
    "#            \"solver\": solver,\n",
    "#            \"precond\": precond,\n",
    "#            \"category\": \"LBFGS\"\n",
    "#        })#\n",
    "\n",
    "## Scipy (2 runs)\n",
    "# for solver in [\"scipy_tnc\", \"scipy_cobyqa\"]:\n",
    "#    configs.append({\n",
    "#        \"name\": solver,\n",
    "#        \"solver\": solver,\n",
    "#        \"precond\": False,\n",
    "#        \"category\": \"Scipy\"\n",
    "#    })\n",
    "\n",
    "print(f\"Total configurations: {len(configs)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-16T10:05:06.853033Z",
     "iopub.status.busy": "2026-01-16T10:05:06.852923Z",
     "iopub.status.idle": "2026-01-16T10:05:06.856374Z",
     "shell.execute_reply": "2026-01-16T10:05:06.855957Z"
    }
   },
   "outputs": [],
   "source": [
    "def run_benchmark(config, x0, max_iter=2000, lower_bound=-5.0, upper_bound=10.0):\n",
    "    \"\"\"Run a single benchmark configuration.\"\"\"\n",
    "    solver_opts = {}\n",
    "    if \"lr\" in config:\n",
    "        solver_opts[\"learning_rate\"] = config[\"lr\"]\n",
    "    if \"ls\" in config:\n",
    "        solver_opts[\"linesearch\"] = config[\"ls\"]\n",
    "\n",
    "    try:\n",
    "        xs, state = minimize(\n",
    "            rosenbrock,\n",
    "            x0,\n",
    "            solver_name=config[\"solver\"],\n",
    "            solver_options=solver_opts,\n",
    "            precondition=config.get(\"precond\", False),\n",
    "            lower_bound=lower_bound,\n",
    "            upper_bound=upper_bound,\n",
    "            max_iter=max_iter,\n",
    "            atol=1e-15,\n",
    "            rtol=1e-12,\n",
    "        )\n",
    "\n",
    "        final_loss = float(rosenbrock(xs))\n",
    "        distance = float(jnp.linalg.norm(xs - 1.0))\n",
    "        steps = int(state.iter_num)\n",
    "        converged = final_loss < 1e-10\n",
    "        error = None\n",
    "    except Exception as e:\n",
    "        final_loss = float(\"inf\")\n",
    "        distance = float(\"inf\")\n",
    "        steps = -1\n",
    "        converged = False\n",
    "        error = str(e)[:50]\n",
    "\n",
    "    return {\n",
    "        \"name\": config[\"name\"],\n",
    "        \"category\": config[\"category\"],\n",
    "        \"steps\": steps,\n",
    "        \"final_loss\": final_loss,\n",
    "        \"distance_to_opt\": distance,\n",
    "        \"converged\": converged,\n",
    "        \"error\": error,\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-16T10:05:06.857498Z",
     "iopub.status.busy": "2026-01-16T10:05:06.857402Z",
     "iopub.status.idle": "2026-01-16T10:05:31.317385Z",
     "shell.execute_reply": "2026-01-16T10:05:31.316199Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running 1/70: adam_lr0.001_nocond... "
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps=2000, loss=5.65e+06, dist=6.61e+00\n",
      "Running 2/70: adam_lr0.001_cond... "
     ]
    },
    {
     "name": "stderr",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps=2000, loss=3.09e+06, dist=1.30e+01\n",
      "Running 3/70: adam_lr0.01_nocond... "
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
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    },
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    },
    {
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     ]
    },
    {
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    },
    {
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    },
    {
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    },
    {
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     ]
    },
    {
     "name": "stdout",
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     "text": [
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      "Running 8/70: sgd_lr0.01_cond... "
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    },
    {
     "name": "stderr",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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    },
    {
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    },
    {
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    },
    {
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    },
    {
     "name": "stdout",
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    },
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     ]
    },
    {
     "name": "stdout",
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    },
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     ]
    },
    {
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    },
    {
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     ]
    },
    {
     "name": "stdout",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
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    },
    {
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     ]
    },
    {
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    },
    {
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     ]
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    },
    {
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    },
    {
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    },
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    },
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    },
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    },
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    },
    {
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    {
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     ]
    },
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     ]
    },
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     ]
    },
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     ]
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     ]
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     ]
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    },
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     ]
    },
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     ]
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      "Running 65/70: lbfgs_ba_nocond... "
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      "steps=1053, loss=3.10e+06, dist=9.62e+00\n",
      "Running 67/70: lbfgs_zo_nocond... "
     ]
    },
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      "steps=2000, loss=3.17e+06, dist=9.32e+00\n",
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     ]
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     "name": "stderr",
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      "100.00%|██████████| [00:00<00:00, 142.44%/s]\n",
      "/home/wassim/micromamba/envs/fg/lib/python3.11/site-packages/jaxopt/_src/scipy_wrappers.py:341: OptimizeWarning: Unknown solver options: maxiter\n",
      "  res = osp.optimize.minimize(scipy_fun, jnp_to_onp(init_params, self.dtype),\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps=2000, loss=3.09e+06, dist=1.29e+01\n",
      "Running 69/70: scipy_tnc... steps=15, loss=3.09e+06, dist=1.30e+01\n",
      "Running 70/70: scipy_cobyqa... "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wassim/micromamba/envs/fg/lib/python3.11/site-packages/jaxopt/_src/scipy_wrappers.py:341: RuntimeWarning: Method cobyqa does not use gradient information (jac).\n",
      "  res = osp.optimize.minimize(scipy_fun, jnp_to_onp(init_params, self.dtype),\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps=246, loss=3.09e+06, dist=1.41e+01\n",
      "\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# Run all benchmarks\n",
    "results = []\n",
    "\n",
    "for i, config in enumerate(configs):\n",
    "    print(f\"Running {i+1}/{len(configs)}: {config['name']}...\", end=\" \")\n",
    "    result = run_benchmark(config, x0)\n",
    "    results.append(result)\n",
    "\n",
    "    if result[\"error\"]:\n",
    "        print(f\"ERROR: {result['error']}\")\n",
    "    else:\n",
    "        print(\n",
    "            f\"steps={result['steps']}, loss={result['final_loss']:.2e}, dist={result['distance_to_opt']:.2e}\"\n",
    "        )\n",
    "\n",
    "print(\"\\nDone!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-16T10:05:31.320135Z",
     "iopub.status.busy": "2026-01-16T10:05:31.319858Z",
     "iopub.status.idle": "2026-01-16T10:05:31.338970Z",
     "shell.execute_reply": "2026-01-16T10:05:31.338437Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      " Adam/SGD\n",
      "============================================================\n",
      "                    name  steps final_loss distance_to_opt  converged\n",
      "     adam_lr0.001_nocond   2000   5.65e+06        6.61e+00      False\n",
      "       adam_lr0.001_cond   2000   3.09e+06        1.30e+01      False\n",
      "      adam_lr0.01_nocond   2000   3.09e+06        1.30e+01      False\n",
      "        adam_lr0.01_cond    488   3.09e+06        1.30e+01      False\n",
      "      sgd_lr0.001_nocond    692   3.09e+06        1.30e+01      False\n",
      "        sgd_lr0.001_cond   2000   3.09e+06        1.30e+01      False\n",
      "       sgd_lr0.01_nocond    692   3.09e+06        1.30e+01      False\n",
      "         sgd_lr0.01_cond   2000   3.09e+06        1.30e+01      False\n",
      "adabelief_lr0.001_nocond   2000   3.09e+06        1.30e+01      False\n",
      "  adabelief_lr0.001_cond   1280   3.09e+06        1.30e+01      False\n",
      " adabelief_lr0.01_nocond   1718   3.09e+06        1.30e+01      False\n",
      "   adabelief_lr0.01_cond    488   3.09e+06        1.30e+01      False\n",
      "     adaw_lr0.001_nocond   2000   5.65e+06        6.61e+00      False\n",
      "       adaw_lr0.001_cond   2000   3.09e+06        1.30e+01      False\n",
      "      adaw_lr0.01_nocond   2000   3.09e+06        1.30e+01      False\n",
      "        adaw_lr0.01_cond   2000   3.09e+06        1.30e+01      False\n",
      "\n",
      "============================================================\n",
      " Active Set v1\n",
      "============================================================\n",
      "                           name  steps final_loss distance_to_opt  converged\n",
      "          active_set_lr0.001_ba     11   3.10e+06        1.57e+01      False\n",
      "          active_set_lr0.001_zo    306   3.10e+06        1.57e+01      False\n",
      "           active_set_lr0.01_ba     11   3.10e+06        1.57e+01      False\n",
      "           active_set_lr0.01_zo    306   3.10e+06        1.57e+01      False\n",
      "            active_set_lr1.0_ba     11   3.10e+06        1.57e+01      False\n",
      "            active_set_lr1.0_zo    306   3.10e+06        1.57e+01      False\n",
      "      active_set_sgd_lr0.001_ba    207   3.10e+06        1.55e+01      False\n",
      "      active_set_sgd_lr0.001_zo    162   3.09e+06        1.30e+01      False\n",
      "       active_set_sgd_lr0.01_ba    207   3.10e+06        1.55e+01      False\n",
      "       active_set_sgd_lr0.01_zo    162   3.09e+06        1.30e+01      False\n",
      "        active_set_sgd_lr1.0_ba    207   3.10e+06        1.55e+01      False\n",
      "        active_set_sgd_lr1.0_zo    162   3.09e+06        1.30e+01      False\n",
      "active_set_adabelief_lr0.001_ba     11   3.10e+06        1.65e+01      False\n",
      "active_set_adabelief_lr0.001_zo    324   3.09e+06        1.64e+01      False\n",
      " active_set_adabelief_lr0.01_ba     11   3.10e+06        1.65e+01      False\n",
      " active_set_adabelief_lr0.01_zo    324   3.09e+06        1.64e+01      False\n",
      "  active_set_adabelief_lr1.0_ba     11   3.10e+06        1.65e+01      False\n",
      "  active_set_adabelief_lr1.0_zo    324   3.09e+06        1.64e+01      False\n",
      "     active_set_adaw_lr0.001_ba     11   3.10e+06        1.57e+01      False\n",
      "     active_set_adaw_lr0.001_zo     20   3.10e+06        1.54e+01      False\n",
      "      active_set_adaw_lr0.01_ba     11   3.10e+06        1.57e+01      False\n",
      "      active_set_adaw_lr0.01_zo     20   3.10e+06        1.54e+01      False\n",
      "       active_set_adaw_lr1.0_ba     11   3.10e+06        1.57e+01      False\n",
      "       active_set_adaw_lr1.0_zo     20   3.10e+06        1.54e+01      False\n",
      "\n",
      "============================================================\n",
      " Active Set v2\n",
      "============================================================\n",
      "                              name  steps final_loss distance_to_opt  converged\n",
      "          active_set_v2_lr0.001_ba      7   3.13e+06        1.67e+01      False\n",
      "          active_set_v2_lr0.001_zo    271   3.09e+06        1.66e+01      False\n",
      "           active_set_v2_lr0.01_ba      7   3.13e+06        1.67e+01      False\n",
      "           active_set_v2_lr0.01_zo    271   3.09e+06        1.66e+01      False\n",
      "            active_set_v2_lr1.0_ba      7   3.13e+06        1.67e+01      False\n",
      "            active_set_v2_lr1.0_zo    271   3.09e+06        1.66e+01      False\n",
      "      active_set_v2_sgd_lr0.001_ba    207   3.10e+06        1.55e+01      False\n",
      "      active_set_v2_sgd_lr0.001_zo    162   3.09e+06        1.30e+01      False\n",
      "       active_set_v2_sgd_lr0.01_ba    207   3.10e+06        1.55e+01      False\n",
      "       active_set_v2_sgd_lr0.01_zo    162   3.09e+06        1.30e+01      False\n",
      "        active_set_v2_sgd_lr1.0_ba    207   3.10e+06        1.55e+01      False\n",
      "        active_set_v2_sgd_lr1.0_zo    162   3.09e+06        1.30e+01      False\n",
      "active_set_v2_adabelief_lr0.001_ba      7   3.12e+06        1.67e+01      False\n",
      "active_set_v2_adabelief_lr0.001_zo    334   3.09e+06        1.64e+01      False\n",
      " active_set_v2_adabelief_lr0.01_ba      7   3.12e+06        1.67e+01      False\n",
      " active_set_v2_adabelief_lr0.01_zo    334   3.09e+06        1.64e+01      False\n",
      "  active_set_v2_adabelief_lr1.0_ba      7   3.12e+06        1.67e+01      False\n",
      "  active_set_v2_adabelief_lr1.0_zo    334   3.09e+06        1.64e+01      False\n",
      "     active_set_v2_adaw_lr0.001_ba      7   3.13e+06        1.67e+01      False\n",
      "     active_set_v2_adaw_lr0.001_zo    354   3.09e+06        1.66e+01      False\n",
      "      active_set_v2_adaw_lr0.01_ba      7   3.13e+06        1.67e+01      False\n",
      "      active_set_v2_adaw_lr0.01_zo    354   3.09e+06        1.66e+01      False\n",
      "       active_set_v2_adaw_lr1.0_ba      7   3.13e+06        1.67e+01      False\n",
      "       active_set_v2_adaw_lr1.0_zo    354   3.09e+06        1.66e+01      False\n",
      "\n",
      "============================================================\n",
      " LBFGS\n",
      "============================================================\n",
      "           name  steps final_loss distance_to_opt  converged\n",
      "lbfgs_ba_nocond   2000   3.16e+06        9.35e+00      False\n",
      "  lbfgs_ba_cond   1053   3.10e+06        9.62e+00      False\n",
      "lbfgs_zo_nocond   2000   3.17e+06        9.32e+00      False\n",
      "  lbfgs_zo_cond   2000   3.09e+06        1.29e+01      False\n",
      "\n",
      "============================================================\n",
      " Scipy\n",
      "============================================================\n",
      "        name  steps final_loss distance_to_opt  converged\n",
      "   scipy_tnc     15   3.09e+06        1.30e+01      False\n",
      "scipy_cobyqa    246   3.09e+06        1.41e+01      False\n"
     ]
    }
   ],
   "source": [
    "# Create results DataFrame\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "# Format for display\n",
    "df_display = df.copy()\n",
    "df_display[\"final_loss\"] = df_display[\"final_loss\"].apply(\n",
    "    lambda x: f\"{x:.2e}\" if x != float(\"inf\") else \"inf\"\n",
    ")\n",
    "df_display[\"distance_to_opt\"] = df_display[\"distance_to_opt\"].apply(\n",
    "    lambda x: f\"{x:.2e}\" if x != float(\"inf\") else \"inf\"\n",
    ")\n",
    "\n",
    "# Display by category\n",
    "for category in df[\"category\"].unique():\n",
    "    print(f\"\\n{'='*60}\")\n",
    "    print(f\" {category}\")\n",
    "    print(f\"{'='*60}\")\n",
    "    cat_df = df_display[df_display[\"category\"] == category][\n",
    "        [\"name\", \"steps\", \"final_loss\", \"distance_to_opt\", \"converged\"]\n",
    "    ]\n",
    "    print(cat_df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-16T10:05:31.340401Z",
     "iopub.status.busy": "2026-01-16T10:05:31.340260Z",
     "iopub.status.idle": "2026-01-16T10:05:31.346460Z",
     "shell.execute_reply": "2026-01-16T10:05:31.345876Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      " Summary by Category\n",
      "============================================================\n",
      "                 steps              final_loss             converged\n",
      "                  mean   min   max        mean         min       sum\n",
      "category                                                            \n",
      "Active Set v1   131.50    11   324  3096446.04  3089881.70         0\n",
      "Active Set v2   168.62     7   354  3104310.62  3089881.70         0\n",
      "Adam/SGD       1584.88   488  2000  3409857.30  3089881.70         0\n",
      "LBFGS          1763.25  1053  2000  3131901.62  3093938.39         0\n",
      "Scipy           130.50    15   246  3087563.82  3085245.93         0\n"
     ]
    }
   ],
   "source": [
    "# Summary statistics\n",
    "print(\"\\n\" + \"=\" * 60)\n",
    "print(\" Summary by Category\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "# Filter out failed runs\n",
    "df_valid = df[df[\"steps\"] > 0]\n",
    "\n",
    "summary = (\n",
    "    df_valid.groupby(\"category\")\n",
    "    .agg({\"steps\": [\"mean\", \"min\", \"max\"], \"final_loss\": [\"mean\", \"min\"], \"converged\": \"sum\"})\n",
    "    .round(2)\n",
    ")\n",
    "\n",
    "print(summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-16T10:05:31.348390Z",
     "iopub.status.busy": "2026-01-16T10:05:31.348218Z",
     "iopub.status.idle": "2026-01-16T10:05:31.589788Z",
     "shell.execute_reply": "2026-01-16T10:05:31.589270Z"
    }
   },
   "outputs": [
    {
     "data": {
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J3z7fuHEDjRo1wqNHj5SeO5CxbVzWunXrxO2Obm5uWLVqldKr54R5e3p6onnz5vj48SNev34NfX19zJ8/H0BGqZCGDRuqvKrw0aNHOH/+PAIDA5GQkID379+jbt26+PjxI54+fQorKyulV3MI87506RLKlCmD4cOHAwBOnz6Nnj17YtiwYTh79ix0dXVx4cIFpccFgPv37+PYsWN4+PAhoqOjERERgTp16uDx48d4+vQpunfvrvRq/8zzFlY7u7q6ok+fPrCwsMDRo0fRpEkTuLi4qDTv27dvY//+/bh27Ro+ffqE+Ph41K1bFxcvXkRAQABMTEzw8uVLlef9/fffo2PHjgAySuAMGjQI7dq1w/79+6Gjo6PUvDOPHxUVJXf5ypUrceDAAQAZ553ly5fj0qVLKo3t5eUFIyMjREZGIiAgAE2aNMGKFSsAZDw3GzZsCF9fX6XnLshclmfTpk3YvHkzgIzXwNWrV8PDw0OluXt6eoqlhkJDQ9GiRQtMmzYNwH/Pzzt37qg0748fPyIpKUm8rz179mDZsmUAMrY2rlu3TunXIdl5N2rUCM+fP0dcXBw6duwIOzs7JCQkwNvbG40aNcLNmzdVmjdjmk72PYnw/ur69evo0qWL+Nru4+ODiRMn5uncxIou4Rh69+4dXrx4AQBITk6GmZkZBg8ejISEBFy5cgV9+vRRy84QVvTIHkMxMTFIS0vDo0ePYGtriy1btuD9+/d4+vQpunbtisDAwAKeLSuMhGPo33//hb+/v1iOcdq0aXBwcMC5c+dw9+5dGBsbi+cpdewkY3nH4WwhEhUVBRsbGxw8eBAPHjyAnp4eVq5ciTlz5uCbb77B1atXcePGDdjZ2aFdu3Y4d+6cSvdz8eJF6OnpwdXVFX379hW3dKelpeHKlStwdHTE/fv3ASj+RI2OjoatrS02btyIe/fuwcDAAOvXr8dff/2Fb775BidPnoS/vz9Gjx6Nrl27qjR3YS5ubm7Q1dXFL7/8IoZjw4cPR5cuXdCnTx80b95c4WA5NjYWdnZ2WLRoEW7fvg0jIyNs374du3btQtmyZbFlyxa8e/cOkydPhpWVVZ7m7erqCn19fdSoUQMhISEAMk6SnTt3hoWFBQwNDRWed2RkJPr06YMjR47A09MTDRo0wOnTpzF79myMGDECCxYsQHp6OhwcHDBkyBC5LXnKztvNzQ1169ZFiRIlxAD2+PHjqFSpEmxsbKCjowNnZ2elxo6KihKDE2dnZ/Tu3RvW1tbYvHkzwsPDsWrVKpQtWxZHjhzBL7/8IoYcih6Psl9CNGjQACVKlMDdu3fF3+eXX36BlZUVmjZtqtTcIyMjERYWBiAjaDc3N0evXr3w119/4e3bt9i/fz+0tLSwe/duVK9eXQw5VJl348aNUapUKXh5eQEAbt26hYYNG6JHjx7Q09NTat5BQUFyYXft2rXRrVs3tGrVCgsXLsT79+8xbtw4NG7cGA0aNFB5W7OzszPatGmDpUuXYuzYsQAy/tYPHz6EjY0NZs6cKQaFijwmQUFB2LZtG4CMEhS1atVCv3790KJFC8yaNQuvXr3CH3/8gYYNG6Jx48Y4ceKEyvNu3bo1Nm3aJJbQiI+PR2BgIMaOHYtFixbB1dVV4Xm/fPkSy5cvB/Df8WZnZwcDAwNMmTIFAQEB2LRpExo2bIimTZuqPG8nJye0atUK+/fvh52dHQAgMTERoaGhmDNnDlavXi1+qaHoMRgVFYVPnz4ByHhcevbsiX79+mHdunUICwvDli1b8M033+Dvv/+Gtra2WN9WlWO8YcOGKFmyJG7cuAEgI6ytXbs2evfuDV1dXaXPKxEREXj//j2AjMfGzMwM3bt3x7JlyxAUFISjR49CS0sLW7duRbVq1XD9+nWV566jo4Nvv/1WfHzv378PXV1dmJubQ19fH05OTgrP+/Pnz2JZGCcnJ3To0AEmJiaYN28eHj58CHd3d2hpaWHFihWoWrUqrly5ovK8dXV18eOPP4pfmj59+hTt2rWDqakpWrRoodS8GSuKzp07h/bt28PMzAwHDx4EABw8eBB169aFhYUFatasifPnzxfwLFlhdvbsWfF9ib29PQAgMDAQpqam6NixI5o0aaLS+3JWfFy4cAF169bFoEGDMHXqVKSlpeH06dMYMmQImjdvDn19fS5BxHLk5uaGatWqoX///jAyMsLLly8REhKClStXolmzZjAzM1P58weTDoezhcjHjx+xbt069O/fH2ZmZmIoAwAHDhzAr7/+ioiICCQlJYn15pT9liMqKgr29vbiaiFTU1M8ffoUABReaZrduDt37oS1tTVMTU3h5uYmXufq6ooaNWrg3bt3SElJEWu1qvINzaVLl6Cnp4erV6/C2tpabNgDZKxO8/DwwMOHDxUePz4+HgcPHsSQIUNgYmIiF77eu3cP2traePbsGdLS0sRak6rM293dHXp6erh9+zaGDRsmt5ry8ePH8PHxgZ+fn1Ljr1ixAl26dMH48ePFFR1JSUlwcXFB9+7dxQ/7CQkJKs/b0dERenp6ePToESZMmCC3qjc4OBj+/v5Kr5iNjo5Gr169sHPnTnEVl6urK5YvX45x48aJDcBmz54NBwcHlT8EOTk5oWnTpnj8+DGmTZsmrhwGMkLWV69eid8WKiIuLg4WFhbiarsmTZrAy8sLW7ZswZQpUzBq1Cikp6dj9erV+P3335UOlgQuLi5o3LgxHj9+jHnz5smtkEtOTkZoaKi4qlXRv+n58+dRrlw5bN68GaNHjxa/fDl37hwcHBxw5MgRpKen49atW+IKdGWPl8DAQDRt2hRXr15FQEAADAwMxNqnmeudKjr29evXUb58eSxduhQTJ04UQ/pLly5h7NixYs0/YeWsKvN+/fo1DAwMcOXKFQQHB0NHR0es/Zq5BqyiY//777/4+eefMWPGDEyZMkUMH2/cuIFJkyZh/fr1ADLOucKqB2Xn/e7dO7Ru3RpeXl6IjIxEvXr1xC+rIiMjVSrsHxsbCwsLC2zevBmenp5o3Lgx3N3dsWbNGkycOFGsVbV48WKMGzdO5SBPaCz2+PFjzJw5E7dv3xavi4uLQ3BwsNKvR/Hx8bCwsMCyZcvg4eGBpk2bwtvbG7t378b06dNhb2+P1NRUbNmyBdOnT1dq9bYsFxcXNGzYEI8fP8bChQvh7u4uXpeeno6PHz+KdcMV+ZsmJibC0tISs2bNknudOH78OBYsWAAbGxskJCTgyJEjWLBggdI7CARubm6oX78+Hj58iBUrVuDUqVNy10dHR4srK3j1BCtuhGP++fPn6NatG06dOoUjR46gVKlSYkD78eNH3Lp1S3zPw88TlpWXL1/Czs4Ot27dQlhYGBo3boxRo0aJ14eGhvK5luXo3r17sLS0hKurK1xdXWFrawsHBwfxc6i/v7+424qPIZaVe/fuYezYsbh+/ToiIiIwf/586Ojo4Pnz5wAyFjMInxn4GCpcOJwtZD58+IAdO3agWrVqmDVrFoCMJ01iYiL69eunlsLxso1EevXqhVevXsHHxwfa2tpyH5KVFRERgQMHDqB27dpfNXAaOHAgAgICVB4byPjQPnbsWHh6egIAunbtKm4JCggIULnhTWxsLI4fP47GjRvLdakHABsbGzHIUtWXL18wbdo0cd49e/YU///Ro0dfbb3NjWwjpa1bt6JRo0YYNmyYuNoNAExMTFT+EC9r+fLlYpBsamoqrh709fWVC8aVtXPnTpiammL06NFiUJWeng4PDw+Ym5uLH36EcE+VF46tW7eKX0L06dNHXCl27949pbcyC06cOIFOnTph+PDh4jZjICN069q1qxgeCseiKvP+559/xC9mhg0bhkOHDgEA7ty5A0dHR5XmDQBnzpxBzZo1YWRkJPdcXLRoEbp3767yuILo6Gjx/BQQEIAuXboAyAhYR40aJa44VpRsg7vffvsNTZs2ldsCuHHjRnTo0AHJycl5mnd8fLzYqfTly5diI7Dr169jyJAh4ip3RQnPTz8/P+jo6KB+/fq4cuWK+Pv8888/MDIyUqn5l6zExESx6WFISAhatGgBIONY7N+/v8qNBQ8cOIBOnTph1KhRWLVqlXj51atX0bVrV7EESF6O8T179ojnwIEDB+L48eMAgLt378p9sacsJycnmJqairshBLdv30bXrl3FkFw4ZlSZ+4kTJ8SVqw4ODmKDwdu3b4vnGGUJ26aF1eWChw8fwtzcXDyX56WTrpubm1hKaMaMGWIDs1u3bonhE79BZ8XZ7du3oaOjI77mAhmr17777jts2bKlAGfGNEF6ejoeP36MMmXKYPbs2eLl4eHhaNasGQYOHMjnWJaroKAgmJqaYt68eQAy3mvdvXsXQ4cOxcCBA1VuRs2Kjw8fPsDQ0BDdunUTL0tISMAff/yBGjVq5LkJNpMWNwQrZKpWrUoDBw6kefPm0f3792nfvn2kpaVFfn5+9PDhQ7HpU1788MMP4v///PPPtHHjRpo0aRLt2bOHWrZsqfK4FStWpL59+9LixYspODiY1q1bR0REDx8+pLt37+a56P33339P69evF5taVahQgcqUKUP3798nMzMzunv3rkrj/vDDD9SrVy9aunQpJSUl0cKFC4mIyM/Pj3x8fPI0Z6KMpi4rV64U5125cmUqV64cPXr0iCwsLOjx48dKjSfb3GfChAk0depUevv2Lbm5udHbt2/pxYsX9P79e6patWqe5z5v3jzq3LkzEWU01alQoQI9evSIRo0apdJ4+P+GNGPGjKERI0bQrVu36MaNG/ThwwfS0tIiU1NT0tLSEhuOffvtt0REKjWmmzBhApmamhJRRhOPH374gR49ekQODg4qN3MbMGAAzZgxg/z8/OjmzZtio7E2bdrQDz/8IBZUFxrqqDLvIUOGkImJCRFlNKn79ttvxXlXqFBB6fGEuVtZWdHOnTspNDSUrl27Jp5LOnXqRGXLlqW4uDilx5ZVvnx5+vXXX4ko47yira1Nnp6eNH78eLK0tKQqVaooNZ6WlhYBoA4dOtDhw4cpISGBPDw8xHkaGxtT+fLl83xOLFu2LNWoUYOIMorl16lThy5dukQTJkyggQMHkra2tlLjCc/Ppk2b0pkzZ6h06dLk5uZG0dHRRESko6NDFSpUoOTk5DzN+9tvvxWf49ra2tS4cWNycXGhCRMm0NChQ6lmzZpKjSccJ3Z2djRhwgS6d+8e3bx5k0JCQogoo9Fg6dKlxedmXo7xUaNGiefDtLQ0Klu2rHiMy74+KcvCwoLmzZtHz549o1u3btHr16+JiKhly5ZUoUIF8fn5zTffqDz3AQMGUMeOHYmIKDk5mUqXLi3O/eeff1Z6PADUtm1bWrlypdiw8Pnz50SU0VhMW1ubnj17RkQZx5aqzM3NqX379kRElJSURCVKlKCHDx/S6NGjxWNFlceDsaKiWrVqlJiYSDt27BAv6969O504cYImT55MwcHBKjdWZEWflpYW6erqkqWlJe3atYs+ffpEREQ//fQTeXh40MOHD+nJkycFPEtWWAnvweLi4qhWrVp08uRJunfvHpUuXZr09fVpwoQJVLJkSfH9AGPZKVeuHI0bN44ePHhAu3fvJiKiMmXK0Jw5c2jYsGEUERFRwDNkOSrAYJjlICwsDNu2bcOvv/6KLl26oFevXipvk86K8O3tiBEj8NNPP8ltzcyryMhIHDhwAPXr14exsTF69+6t1rkLxo8fD3t7e7Ro0UKlhjeZxcXF4cSJE9DV1YWOjg4sLS3VMm5mM2bMQN++fWFoaJin8WVX0G7cuBEGBgYwNjaGlZWV0s3cFLFo0SJMmDABrVu3zlO9NdmVA8ePH0f79u2xc+dOPHnyBE+fPkX9+vXV3iThr7/+gr29Pdq0aaPy3GXnffHiRbRv3x5r167FzZs38eTJE9StWzdPK8+zsmnTJgwYMABt2rTJ06pZ2bmfPHkStWvXxrBhw7Bt2zY0btxYrcdLWloaXr9+je+++w516tQRV0KqumJEtnlUnTp1YGVlhbVr10JXV1fllYrZzfvDhw8oW7YsatSooXSt1qzGAzJKLjRs2BBdu3bF0qVL0bx586+2lOcmpzmkpaUhOjoaFSpUQOXKlfM0b9mfEeoubtmyBQ8ePEBAQADq1asn1m5Wl7Vr18LW1jbPx7isK1euoH379li5ciWuXLkCPz8//Pbbb3la7Z+Vbdu2oU+fPmp7fvr6+qJTp06YP38+nJyc8ODBA/z2229ibVx12b9/P7p06YK2bduq7TFnTNMIz7v4+HixKWlwcDCaNGmCQYMGyd1WdmcSYwLhGHr//r1c2a9hw4ahVq1a+Pjxo3iZsCWdMVnCMRQSEoL4+HgkJiYiKioK06ZNg5WVlfh5KCUlRa5pOGMC4RgKCAhAQECAWLLv0KFDaN68Ofbs2SPeVvhswqv4Cy8OZwuxiIgIrFu3Dt27dxebrqibk5OTUg16FBUbG4vdu3ejd+/eag+shHlOnjwZ5cuXl6vfqgjZUDOzL1++4OjRo+jfv7/K885ufGHe8+bNw/fff6/y1vrs7mv79u1o2bKl+AZR3SfeqVOnQktLSwzy8zK+7M/+/fffMDIygr6+PqytrVVudJeTpUuXQktLS+wqn9egEMiox9umTRs0btwYtra2agl9M9u2bRu0tLTyHBRm/lknJyeUL18eQ4YMETtOKzp2Ts8fWcOHD1e5rmdmwtxu3ryJKlWqoF+/fuK2HGWbIuVm8uTJYvkOZeeXmfBYPX36FHXq1EG3bt2UqumbuWRDVo+9ME5e6pFmNR4AHDt2DMbGxtDV1YWNjY0kz801a9ao5bmZmfAFSoMGDTBs2DBJQsh9+/ap/fl5+/ZtdOnSBXXq1MGIESNU/mIzp7mcPXsWWlpaSh/njBUVwvPDxcUF5ubmMDY2xowZM8Sa0YaGhrC0tMz25xgTODs7w8DAAEZGRjAzMxNfy4YOHYqffvpJDGj52GHZcXV1FRfXtGjRAsHBwfj8+TNmzZqFrl27qv1LcVb0XLhwAU2aNIGNjQ0qVKiAf/75BwBw5MgR1KtXT+xNwwo/DmcLudDQUKWaiwQGBor18ApaeHi4WMtRijclN27cULiDenh4OKKiohAfH5/ruLGxsWLHb0Xnrcz4/v7+SgcROdUalA1sXr16Jf6/Mo95TuPLNsqQDazz+jeV/fkjR46gZcuWKjV1UqQO49u3b8Xawenp6UqNn/m2sv8W3lAJNVwVGTc0NBR+fn5ytUyz+x3CwsLkQkhFxs8pPJX9+bNnz4p1PxXx8eNHcXWRIgHt58+fv7rPnOR2O+F6b29vpeb94cMHsd6tIvNWpkC+IuGpcNm///4r19wtN/7+/hg7diymTJmCEydOiCu3svsdhBUV6jjXyo5x5swZSZ+bISEh4hcEyo6fG09PT7Rq1UqpBj7CbRSZ+6dPn/DkyRPx5/JyXpG97P79+2jfvr04tiJCQkJw7do1BAUFicdldr9DVFSU3N+TQwNWHHl5eaFp06a4fPkyfHx80KlTJ0yePBlARqPIxo0by52bGMvs3r170NXVxd27d5Gamor58+dj0KBBCA4OBgD07dtXqfcrrPi5d+8e6tevDy8vL7x58wZTp05F5cqV8fHjR7x8+RIzZ85U+25CVrQ8f/4cjRo1EvsqODk5oWLFiuLiu0OHDuHatWsFOUWmBA5n85mU4emzZ8/w7bffomLFigqVKRC22MTExCjUdfv58+dwcnKS7IOcMuPL3ia32/v7+6Np06awtraGjo4OPD09xUZT6qDM+LLBSm7zfvr0KZYsWSL+O7eAVhhP0b+PMuPLXq/s31+R4BeAGOQrch8BAQGYNm2a+HgqOndFwjlFwlPZ+ck2uspt3o8ePULz5s1hYmKCfv36Ydy4cdneh7LzVjQ8zSlszs6TJ09Qv359DB48WGxgl9N9CNclJibm2rBLmfBUmee9MO86deqgd+/eYrMyReb95cuXXJsuKBOeKrraWPDq1Sv88ssv2Lp1K2bMmAEHBwf06dNHPCYzjyf8OyEhQa7hY24UfW4KX1ZlvjwrAQEBmDhxovj4qfO5KXv/ijbHkt2OrMhrxbBhw8SVTuqcu6LhqTDHqKgohef9+PFjNG3aFGZmZujTpw8GDhwonguyug9hvrKvG4wVNzt27MC6devEf4eEhEBbW1tsCMaNd1huPD09MXbsWPHf6enp6Ny5sxjyy17OWFZOnjyJiRMnAvjvOBk4cCDWrFkDAOJrOWPZefToEXr16gXgv2No9erVGDBgAIC8NZNl+Y8bguWj58+fU5MmTcjKyoouX76c6+3T0tKIiCg2NpacnZ1zvG1ycjJt376d1qxZQ8uWLaMJEybQpUuX5G6D/y82Loz9zTffUFRUFFlaWlK1atVynXvz5s1pwIABdPr0aaXmfuLEiVxvr+z4QlOG2NhYOnnyZLa3+/z5Mw0aNIimTp1KR44cITs7O5o2bRqdPHkyy0ZCwrxjYmLowIEDco+ZOsYXxouJiaG///472/GfP39OnTp1or1799Lo0aOJKKMhlzC/rMbV0tKi6OhocnJyotTU1Bznrez4aWlpVLJkSYXHf/r0KU2fPp3S09NzHFdo+pSenk41atQQH4+cGtO8ePGCOnfuTI6OjmRjY5PrfcjO/fjx4zk2pnv8+DH16NGDJk2aRJMnT6bx48cT0dePjTDvtLQ0qlKlikLzTkhIoClTptCcOXPIw8ODpk2bRm/evCELCwvxPmSbjQjzjoqKov379+fYQOrff/+ltm3b0tixYykiIkKuaVxmWlpa4t8vOTk528dNEBUVRePHjycjIyOqWrUqzZw5M8f7SEtLoxIlSlBUVBSZmZlRWFhYjvNu3bo1OTg40Js3b3KcN9F/z/ukpKRcG2rFxMTQtGnTqF27dtS0aVOaO3dujvchO+9OnTrRhw8fsh379evX1LVrV2ratCl988035O7uTg4ODvT+/fssxxeOjy9fvijUeO3atWvUpUsXmjBhAq1Zs4amTJlCv/zyC40bN44+fvwo1xhKdt4dOnTIcd5EGc/NSZMmUWJiokLPzbS0NKpevbpCx3hgYCB17tyZ3N3dycbGJtf7kD3GDx8+nGvTyICAALK3t6dPnz7lOK7s+D///LNCc3/58iV16dKFHj58SLa2tvTx40eF5757925KSkrKdmw/Pz8yNzenP//8k6ZPn05Dhgyh2NjYLMcXnp8//vijeBzlNO/ExESaP38+zZs3j9zd3Wnx4sVUpkwZMjMzE+8j83lFOF42bNhAiYmJ2Y7NWFEinAeioqKIKOP9m+z7U21tbRo1ahSVLVuWiEjpxqGs6BOOobdv31JcXBylpKSQo6MjRUZGElHGudre3v6rz1TcbJEJhGPo8+fPRJTRMM7d3Z3u3bsnHicNGjQQ/79cuXIFM1FWaAnHUGBgIEVHR5O2tjb5+/vT7t27xeOmWrVqYhPpkiVLFtRUmSryPQ4uppKSkvD7779j8+bN2LZtGxo0aPBVfUDZb1aFbzkiIyPRqVMnherNPH36VNxK89dff6FBgwZwc3P7alWPsGonMjISnTt3znWrbUpKCubMmYMtW7bg+PHjqFOnDo4fP67Q3Dt27Ig7d+4U2PiRkZGwsbGRW723f/9+GBoaiqUFMq++FMZVpM6vFOOnp6dj+/btmDFjBp4+fQpzc3OMGjVKvF62qUB6ero4blRUFIyNjcVtDdmRevznz59DW1sb9erVw8CBA3Nd3So7/tGjR3NdaXnw4EHMmjULL1++RN++fTFgwADxPnKae+vWrXPc1hEfH49OnTrh+PHjSEtLw40bN9CjRw/07NlTvI3sc0n277l3714kJSXlOO+IiAi0atVKrkxBaGgounfvjhEjRmT7mLRt2zbH52hkZCQ6dOiAwYMHY+rUqRgxYkSOq1tl592uXTvxnJGdz58/w9XVFZ8/f8bVq1cxYsQIjBgxQixbIEt4/CMjI9GlSxd4eXllO250dDS6du2KoUOHYt68ebCxsclxdavsvFu1aiVXwiMrUVFR8PT0RHh4OG7fvo0JEybAxsYGL1++zHXeue1uOHjwIIYPHy7++99//8XEiRPRu3dvuZXUmefdokULsVB/Tq5duya3HR/IWNU5btw4sbC/7PEdGRkJMzOzXM/lL168gLa2Nho2bIg+ffrkurpVdvyDBw/m+tw8evQoZs+ejTdv3mDQoEGwtLQU7yOn52abNm3g7e2d49hBQUGoUaMGmjVrhi5duoiPsyJz37FjR667JZycnDBv3jy8f/8eDg4O6Ny5s3gfOc09t+fnly9f0KtXLxw5cgQA8PDhQ9jZ2aFly5ZZrnSXnffatWuRkJCQ47zj4+PRoUMHsb5zeno6YmJiMGzYMPTu3VvuvCQ77/bt2+f4/GSsKBHeQzo7O2Ps2LH4/PkzEhISYGZmhsGDByMmJgZXr15Fo0aN1N6AjxUNwjHk5OSEnj17ivXjHRwcUL16dVy6dAmnTp2Cjo6O2AiVMVmyta5NTEwQHByMsLAwzJ49Gw4ODjh9+jQePHgAPT09fn1mObpw4QJ0dXXx6NEjpKen49ChQzA3N8fvv/8OFxcXNGvWTG19P1j+4nA2H0kRnkZFRSEuLi7L7ZXCffj6+sLd3R329vbidRERETAzM8v1A7HgzZs3Yhiyd+9e1KlTB8eOHRM/tAovOMoGv1KPHxERAR0dHWzfvl3u8vXr16Nu3bpinUbZD62dO3dW+HGRavzY2Fh8/PgRaWlpuHPnDszNzTFy5Ejx+sxbqKOiomBqaqpwTRkpx5cqPBV8+fJFnJ+fnx/69u2L/v37i8fGhw8fxPGVmbtU4amsWbNmYdq0aXJbrX19fdGnTx+xBqRsOKPIvKUKT2VDL9n/v3LlCkaMGCGGk69evYKHh4f4eAvzzu0Ylyo8zS5AFO5j0KBBSE5ORlBQEJycnL56vBV57ksVngqCg4MxbNgw7NixQ67kw5IlS+RCYWHsTp06KTRvVcPT1q1bKzR+YmKieGwHBARg0KBB6N27N+Li4gDIl0cQ5q7oeUWq8FSQkpIifqnx6tUrODg4wNTUVJxz5i8xFJ17foSn69atw9ixY+W+sHj27Bn69OmDBw8eyI0tvHZy/TFW3Li5uaFZs2Zyrx+BgYEwMzNDp06d0LJlS4VKfLHiy8vLC3p6erh586bc5fPmzYOVlRV69erFgQjLkbu7O3R1deXKD96/fx9r165F8+bN0bt3b5w9e7bgJsgKvRs3bqBJkyZy722joqJw/fp19OrVC5MmTRJfy7ikiubhcFZiUoan/v7+Yof7li1b4sGDB3IfUgFgz549qFixIqpWrYqjR4+Kl0+cOBGXL1/Oce7h4eGIjIwUP1jLPsGFANXDwwMXL15Enz59xN8xIiJCoQBS6vGF8dzc3FC7dm0cO3ZM7vIBAwbgzZs34u0jIiIUDjmkGD8sLAyBgYFywR2Q8aH6zp076Nq1K6ZPn44bN27A1tZWDCGFACK3eUs9vkCK8DQ0NBSPHz/+KtxJS0uDn58frKysMGLECHh4eKBfv35ibc7o6GiFVuUJpAhPZV24cAF2dnbYsmWL3H106dJFbkVydHR0roGYlOFpQEAApkyZgoULF8LZ2Vlu9V5aWho8PT0xZcoUmJiY4OeffxYfg4SEBFhaWhZYeCrUgf3999/l6sAKbt26hXnz5qF169aoUKGCGH59+fIF/fr1U7geuFThqawjR47A3NwcO3bsEFfbXrx4Ef379xfPmYmJiRg0aJDC85YiPA0NDcX9+/e/Ci/T09MREBAAa2tr2Nraws3NDX369BFvp8gxLkuK8DSrOrCCly9fYtSoUejduzdOnTqF7t27i+GnsnOXOjy9du0ahg8fjr/++ktcfQ4AvXr1grOzs/jvmJgYpebNWFGRnp6O4cOH4/z58wC+fh2KjIwUa03zh1mWnRUrVmDLli0AMl5PM+/cEL7s5GOIZSUtLQ3z5s3DiRMnAOCrHT2xsbFi3wA+hlh2Dh48iFWrVgHI+jwk4GNIM3E4KyEpw9OwsDA0bdoUe/fuRVxcHGbMmAEjIyOcPXsWSUlJch3Cy5QpI3bsE56ouW3BVqTJ1fnz51GuXDlUqVJFbu7z5s3LNfiVevzMDhw4gHr16omr2q5du4a6devKbTFeuXKl0uOqa/xHjx6hRYsWMDExwcCBAzF37ly565OTkxEcHAxdXV2UKlUKZ86cAZDxQr9p06Zct+FJPb6U4WlOTbTS09ORkpKCiIgItGzZEt988w1Onz4t3veuXbu+WuGQE3WGp9nZtWsXhg0bhvnz5+PZs2fw9/dH48aN8fDhQ/E2Bw8ezLGshpThaVBQELS1tbFu3TqMHj0adnZ2sLW1/arR1OrVq/Hjjz+KH3aBjOA9p237UoanWTXRsrS0lGvsBgAbN25EhQoV4OjoKF4WGRmJoKCgbMfOihThKSD/Zurw4cOwsbGBmZkZZsyYgerVq+PcuXPi9QkJCXJhXFakDE8fPXoEfX19dOrUCVZWVnKr74UVrDExMWjTpg1KliwpPjfT09Oxb98+3Lp1K8e5Sxme5tREKz09HWlpaUhOTkaXLl1QsmRJnDp1Srzu8OHDCpW9EeRHeHrixAnY2dlh6tSp8PLygp+fHxo2bChXEunMmTMKlUhirKhJTExE+/btsW/fPgD/hbO3bt0SvyBhLDejR4+We50DAG9vb/zzzz9ITk7mMITlysHBASNHjpQ7Vq5evarwzirGtm3bBn19fbkdklevXsWWLVtyLT/GCj8OZyUidXj64cMH2NjYyF22fv16tGrVCrdv3waQsVJz+PDh4vaI9PR0hd44fPr0Cc2aNcO+ffuQlpaGVatWQV9fH//88w++fPkijvH06VP88MMP4odL4fLMAXR+jp/d75eeng5nZ2f8+uuvGDx4MOrVqycXKgGKddyWYnzhA/mJEycQFRUFd3d3tGvXDsOGDZO7nZubGypVqvTVsRIfH5/jnKUeX8rwVJE6sABw6dIlVKxY8au5q9JtWR3haVZkj52zZ89i6tSp0NHRgZGR0Vc1lnN6/ksZngLAzp07MWHCBAAZH2Dv378POzs72NjYiMdCaGgoqlevLhdY5fb8kTo8VaQO7KdPn1CnTh1x1YLsFnhFqTs8Bb4+N8jex7Nnz+Ds7Ix169aJoZ0ijzcgbXiakJCALl26iDsGfHx8YGlpiS5dusjdzt3dHRUqVPjquZlbDVgpw1NF6sACGZ24y5cv/9VrkCpvfqUKT2WPFQ8PDyxfvhy6urro2LHjV+cV7tjLigvZ56rwfN27dy/MzMzE9xw3b95Eo0aN+AsLpjA/Pz80aNAAGzZsQHp6Oq5du4aGDRsq9QUsKz6E81BMTIz4pb2bmxtGjRqFkydPAgDu3r2Lhg0bypU5YCwnb968wZgxYzBr1iw8f/5cLHMg+2U/01wczkpEyvAUAN6+fYt69eqJH0gFc+fOhZ6enhhgCjVPlRlbkSZX0dHRGD9+vBhEFPT4ERERWZZHyCw0NBSvXr0St6grOm8pxw8LC4OxsbG46jQ9PR2BgYFo06YNpk2bJt5uxYoVYpigaDgj9fhSh6eK1oHdtWuXXFkJRR8bWeoKTz99+iQeK5llnpfQDEC4f0WORanCU4GzszPatm0rrkJMT0+Hr68vhg8fjsOHDwPI+P0DAwOVmrfU4akidWBTU1PFUiPKrHCRIjz18/MT/45Z3UdegzSpw1OhFq3QkDE9PR0fP35E7969MWTIEPF2Bw4cEJ8/ih6HUoenitaBPX/+vPgBSpnXOFnqCk9DQ0OzrCMNfH3sfP78GREREXmaN2OaSrb5l52dHdq2bYtjx47B29sb69atQ7Vq1TBmzBg0adKEa8wyhQnH1cWLF/Hbb7/BysoKLVq04ECEZUm29F2nTp3QtWtXjBw5EqGhoVi2bBm6dOmCDh06QF9fX24xAmOKcHZ2xujRo6Gvr4/OnTvzMVSEcDgrESnC0/DwcERFRYkr5A4dOoRff/0VFy9elLtd3759v9pergxFm1wJQZCyH/7UPb6fnx8MDAwwd+5cREZGij8jUHQFaEGNDwAjR47E/Pnz5VY/3rhxA1ZWVl81SFLlg7ZU4+dHeKpIHVjZuec2fynD0ydPnqBMmTKYMGFClvch/Hx0dLTKgYkU4alsmBUSEgIbGxv8/fffYsCTlJSEmTNnYvLkySrNGZA2PAWUqwOrCCnD08DAQNSpUwdaWlpyYWnm+3j8+LHYEVpZUoangnnz5uH3338X60gLc+7Tp89X81bmdSI/wtPs6sBaWVl9tc1ZkbGlDE///fdffPPNN7CysvqqFIisiIiIXHfdMFYceHh4oFmzZvD09MTatWvRv39/bNiwAcnJybh58yZu3bqFR48eAeC6fEx5UVFReP/+Pd6+fQuAjyGWtatXr6JRo0ZwcnLCkydPYGRkBFtbWwAZx9Djx4/l3sszlpvMx8mnT58QFRWV5XVMM5UgpjYREREUHR1NcXFxVLNmTVq8eDFNnz6dLl26JN5mxYoVVLduXfr06RMREZUvX56IiLS0tEhLSyvbsQMCAqhjx440ZswYatmyJXl6elLfvn1p9uzZNGHCBHJ0dCQiouvXr9OTJ08oOTlZpd8BAFWsWJHWr19Pq1evpuPHj4uXT506lQwMDCgyMpKIiMqWLavQ3KUcPzw8nMaMGUNVqlSh2NhY2rRpE0VFRZGWlhYBEH8+KCiIZs2aRQkJCUo9HlKN/+HDB3ry5AmFhIQQEdGAAQPo9evXdPz4cYqLiyMiIn19fYqOjqbY2Fi5n1XksZZ6fEHFihWpQ4cOdODAAfr8+TNpaWlRtWrVaMWKFRQREUHPnj0jIqLRo0eTtbW1+JiVKKH4qcfExITCw8Pp2LFj9PnzZ3Hu8fHx4r9l557T/P/991+qVasWzZ49m+Lj47+6XvjZmJgYAkC//PILValSRaGxIyMjaebMmTRy5Ei6ffs2LVq06Kv70NLSomfPntFff/1FUVFRCv3+REQpKSni/zdv3pxq1apF3t7eFBkZSVpaWqSjo0M///wz3b59m4iISpcuTXXr1lVo3gEBATR58mSaOnUqnTp1iipWrEh9+/alw4cPk7OzMwUHB1Pp0qWpbdu2FBERQV++fBH/jsqoXbs2NWjQgK5evUofP34kIqKGDRtS9erV6ebNm0REVLJkSapVq5Y4b2X88ssv1K1bNzp37hydOXOGAgMDqUSJEmRsbEyxsbEUFxen8LyDgoKod+/etH37duratSsRZRyz6enp4m1KlixJfn5+5O/vr9Q8iYhu375Nw4cPp/T0dPrw4QN16dJFvI+0tDTxfg4dOkTR0dFKj09E9OOPP1KnTp3o8OHDFBYWRlpaWlS5cmVatmwZxcXFifO2s7OjgQMHKvTcfP/+PV2/fp2CgoIoNTWVrKys6PPnz3Ts2DEKCwsjIiJdXV368uWL+BonUOZ1omzZsmRpaUmOjo70+vVr0tLSonLlytH8+fOpZMmS9O+//xIRUe/eval///5y52JFtWzZkpKSkuj48eP05s0bIiJq0KABpaam0vv375Wau7+/P9WsWZMcHBy+OicR/feYRkZGUnJyMlWqVIkqVqyo0NgxMTG0YMECmjNnDsXFxdGUKVOyvI+goCD6888/VT5eGCtKPDw8yM7Ojjp16kTTp08ne3t72rx5Mz19+pRat25NrVq1ombNmhGR8q81rHjI7v1Ceno6/fjjj1S9enWqWbNmPs+KaZLnz5/TmDFjyMLCgpo0aUJ37tyh27dv0759++jHH38kXV1dql27NhHxeYjlLLv3uT///LOYJbGigcNZNZEyPP38+TMNGjSIpk6dSkeOHCF7e3uaPn06nT17lmxtbemPP/6gcePG0bBhw8je3p5Wr15Nv/76q0q/h/CkNzc3p8WLF9OCBQto7969pKWlRdevX6cHDx7IBUUFPX58fDzNmDGDTp48Sa1ataJXr17Rpk2bKCIiQu4ElpaWRmPGjBED34Ic//Hjx9SjRw+aNGkSTZ48maZNm0bm5ubUqVMn8vT0pE2bNlF4eDi9ffuWPn78SCVLllRqzlKPn5k6w9OsdO/endq0aUN3796ljRs30vPnzykgIIDevXtHP/zwg8LjSBmeEmV80WJra0tbtmyho0ePkqurKy1atIhiY2PlQr3KlStT//79xXAmN1KGp69fv6auXbtS06ZN6ZtvviE3Nzeys7Ojbt260ahRo8jZ2ZlGjx5NS5YsobFjx5K1tTWVKVNGpTeR6gxPZaWnp4s/Z2NjQ8OGDSNvb28aN24czZw5k+zt7WnIkCH0ww8/KDxvqcJTYZ6DBw+m0aNHE1HG8zUsLIzMzMyIKCP0/fDhAxERrVq1ilq3bq3w+FKGp35+fmRubk5//vmnGHbo6uqStbU13blzhzZu3CiG1W/fvqUff/xR4XlnRZ3haVbatWtH3bt3p4CAANq0aRNduXKFnjx5Qs+fP6eqVasqPI7U4Wn58uXJzs6OFi1aRKdPn6ZXr17RlClT6OPHj3LPlzp16tDIkSOpcuXKSo3PWFEDgGJjY+nVq1dElPH+rGfPntSuXbuvznuMCYTzqbBgIbvXFOF9gCwO1lhWwsPDac+ePeKiGCIia2vrPL8/YkWXcB568+YNvXjxQnzN0tLSkvssSUTieUiVBQqsEMvHVbpFVnYNrg4fPozo6Gj8/fff0NbWxtChQ1G3bl25hjGKyKpG6759+9CiRQuxpMGzZ8/w9OlT+Pn5AVB9abvs9ktFmlwVhvFDQ0PF/z98+DDs7OywcOFCAMDr169V3hYsxfhZ1Wg1NzfHwIEDAQCOjo6wt7dH06ZN0axZs69qEhb0+NlRpImWMoTjV3breG51YHOTmpqKQ4cOAQBevHgBHR0dTJ8+HTExMXLHZXh4OO7fv6/U2Fk1qXv+/Dl0dHQwdepUABkdfW/cuKHUuJmbaI0cORIDBgxAbGwsTpw4gYEDB6Jbt274888/Ua1aNaXr52VVB3bcuHHo168fEhISEBgYiMOHD2Pp0qXw9PQEoPy5JS0tTakmWrl59+4dnj59iidPnnx1P4Ls6sDmRvY2slv1dXV10blzZ/HfmZuYKevmzZtiiRTZ+7C0tIS3tzeaNWuG169fK1ViQLaBlqWlJWxtbZGYmAhHR0cMHjwYc+bMEcskNG7cGL6+vgqPnVUN2CFDhqB9+/ZITEzEjRs3MH36dOjo6MDY2Fht5xVFmmjlJqu/u+zjmlsdWEWcO3cOKSkpYnM1W1tbhIWFyd13WlqaWP5FUVmdV2JiYtC2bVsMHjwYAODl5fVV+STGihPheRYWFobw8HAAGaWPvvvuO2zevBlpaWm4efMmGjRoIJYyYEyWcAw5OjrC2toaw4cPh6urq1hmTZbwvjQiIgILFy7kUjIMwH/H0Js3b8SyYmlpaRg2bBjs7OwQEhKCO3fuoGnTpuL7Usaycv78eRgbG8PIyAjW1tbYsGHDV7cRzkORkZGwsrLK8lzFNBOHs2ogdXiaU43W33777asP+Yp69uwZ1q9fj+3btyMwMDDLQAzIuslVYRgfyGhms3fvXrF5TXp6Ov755x9MmjQJgwYNQsWKFXPs2p3f42dXo7VLly4YP368eLugoCAxAFJHDVh1jS9Qd3jq7++PCRMmYNq0aV81V8h8vGSuA6sIqcJTWSdPnsSkSZMA/FfD9enTp2jRogX69OmDChUqKP3FjNThaXZ1YEePHo39+/crNVeBlOHpkydP0KxZM/Tv3x96enpyDe0A9XWjlyI8FX6/GzduoEGDBmJdU9ljs2rVqvj2229x+vRppeYrdXiaXQ1YW1tb9O/fXzzeQ0ND8fHjR7nfNzdShqf+/v6wtbXFuHHjsGvXLrnrMh8rmevAKiI/wtNTp05hwIABSE9PF0OAyMhIdO3aFe3bt0elSpU4nGXF3tmzZ2FsbIwGDRqIr13Xr19HjRo1MGDAAOjr63PjJpYjZ2dnNG/eHM+ePUPbtm1hbGyMHTt2yIUewutGVFQUTE1N4eHhUVDTZYWQo6MjateuDVNTU/To0QNAxueAQYMGQV9fH23atMnzIidWtD169Aj6+vp49uwZPn36hIMHD8LW1lZs2gvIB7NmZma4cuVKQU2XSYDDWTWQKjwF5Ls91q5dW66pEgAMGDBAbKCjjOfPn6NGjRpYtGgR2rdvD3t7eyxZskR8wgsfOlVtciXl+MIYDx48QI8ePXDr1i0A8h/oZ8yYgR9//FHpMCw/xs+uwVXv3r0REBCg9Hj5Mb6U4enz58/RpEkTLF26FOvWrUP16tWxZs0auZWLQN6aaAHShKcCf39/dO7cGdevXxcvE47xv//+G1paWuKq1oIOT2Wpu4mWlOFpfHw8WrdujX379gHIOG5+++03jBs3Tu52qjbRkio8lf173717F5MmTRIDRtlzip+fH2rWrCk+v5Q5TqQMTwXZNdCytLRUeqW5QMrwNDAwEDo6Oli9ejWOHj2K2rVrY+bMmXLzB/LeREvK8DQgIADdu3fHzZs3xcuE49HZ2RklSpRQ6XhhrCgQjvkXL16gY8eOePDgAY4ePYoWLVpg69atADLOG+/evZNrkMlYZuHh4ejXrx/8/Pzg7OyMVq1aYcGCBdDT08OWLVvkmj1GRkaic+fOvPqRyQkICMC4ceNw+fJlREREwNDQEGZmZuL1wcHB4sp+Pg8xQeamsBcvXoSlpaX478jISAwdOhTLli0DIB/Mmpqa8nmoCOJwNo+kCk+zcuDAAdSrV0/sbH7t2jXUrVsXL168UHqsefPmYe7cuQAyVvocO3YMQ4cOxeLFi8XAIDAwEBMmTFApQJVi/I8fP4offl+8eIGGDRuKqx5lVy2/f/8ev/zyixigKNrFW+rxBRcuXICdnR22bNkiF6B26dIlTys3pRpf6vB0z549cqt6r127hvbt22PVqlXiZU+fPsXcuXPFcEZZ6g5PP3/+LHbpffHiBaysrNC1a1fxeuEYDwoKQuvWrVU+VtQdnmblyJEjMDc3x44dO8RzycWLF9G/f3/ExcUpPF+pw9PExERYWlri3bt34mWhoaGoX78+Jk+eLF42d+5cuTArN1KGp2/evMHEiRPFQHTBggWoXr06Zs6c+dV5b9euXXLHYGEIT2Vdu3YNw4cPx19//YXXr1+Ll/fq1UulFWlSh6dnzpzBqFGjxH8HBQWha9eumDlzpvjcf/HiBX7//Xfx76MsdYenYWFh4g6SN2/ewNLSEq1atRKPFeF4fPPmDTp16iR3XmGsOLp16xZMTU2xZMkS8bJz586hZcuWWL9+PWJiYgpwdqywynzOTE1NRXBwMAICAmBoaCiGsQYGBhg4cKD4OS4mJgYdOnTA1atX833OrPAKDAxEuXLlxM+MQMbrdcuWLWFgYFCAM2OFnXAu8vb2RlpaGnx9fWFmZoanT5+K7yd37dqFGTNmiLeNjo5Gx44dOZgtojicVSN1hqcCqWrA7t69G8OGDRNX9X758gXHjx+Hvb29+EH+yZMnStfIk2r8+Ph4jBkzBiNGjEBSUhLi4uIwZswYVKpUSRxDeKw+fPgghj+KhhxSj5+Zumu0Sjm+1OHpvn375L4lFO7jl19+wZkzZwBkbCFTJmCSMjxNTExEnz59MG3aNAQHB+PLly+YO3cuWrZsiePHj8ut+EtJSRFXK6t6rKgrPJWl7jqwgHThqayhQ4eid+/ecpe9fv0a9evXV2lMqcPTyMhINGjQACNHjkRCQgIAYMWKFTA3N8fFixfx5cuXr35G1aBN3eFpVtRRA1YgdXh68uRJtGvXTu6ywMBA1KtXD5s2bQKQ8VgLpYYUIWV4mpqaChsbG9jZ2eHZs2cAMnbftG7dGnv37kVcXJzc7YOCgsSxOZxlxVVYWBj09fXRsWNHudWNp06dgp6entoWR7Ciyd/fH4GBgeLK6gcPHmDQoEGIjIzE/fv30adPH7n3nqdOnVLp9Y4VfdOmTcMPP/wg95k/LS0Nenp64g5MxmTJBrNaWlpYtGgRAMDGxgaWlpbYtWsXTp8+jfr168Pd3V38OV9fX9y+fbsgpszyAYezeaTO8DQ/arQK83V0dES/fv1w69Yt8bKYmBhYWFhg6dKlCo+XX+OnpKTA0dERw4YNw5QpU5CcnIzU1FRMnz4dRkZGcjVWVSH1+FI0uMqv8aUIT2XnHB8fDz09PcyePVvu+u3bt2Pw4MFyq5YVkR/h6a1bt9ClSxfMnTsXnz9/RlJSEubOnYthw4bh7Nmzea59qklNtGSpOzyVde/ePdy9exc2NjaYMWOG3HX29vYq1ZaWMjyVbRrSsmVLDBkyRLyPuXPnwsLCAk5OTlneh6o0pYEWIE14KvyMoHv37rC1tZW73tHREX379hXriCsqP8LTV69eoU+fPnIroNesWYNBgwbh4MGDiI2NVWrOjBVlwjn248ePMDQ0xJgxY8RtwwDE3SaMCd69eyeWhHJ3d8dvv/2G/v3745dffsHRo0cRERGBNm3awN7eHjVq1ICrq6vcz/MXYSwn06ZNg7a2tvj5nLHcuLi4wNDQEObm5nI7QBYsWIAxY8agf//+4nmIv4wvHjicVYKU4anUNWBleXl54ejRo5gwYQLatWuH27dvi1u/9u3bh9mzZ+cpXJJq/JSUFLi5uWHw4MH4/fffkZycjC9fvmDevHnQ0dHJcxdedY4vdYMrqceXva26w1NZR48exdy5c3H37l20bNkS8+bNk7vO1tZWpRciKcNTYT737t2Dqakp5s6di/DwcHz58gULFixA//79la4xWRSaaEkRnsrWgW3UqBFu374Nd3d3DBgwAIMGDUJERARu3LiB+vXr486dO0qNnR/hqXAf4eHhaNmyJYYOHSqON23aNHTp0kWu9IgiNLmBVubbqjM8lXXs2DEMGzYMz549Q+fOnWFnZyded/bsWfTs2VOlUglShqfC3/Dt27ewsLDA2LFjxRXQ69atQ+/evbF//36lmtAxVtQJ56UPHz7A2NgYQ4cO5bqOLEtpaWk4dOgQ2rVrhyVLlsDCwgJ37txBSkoKzp49i5IlS+Lu3bt4/vw5PDw81FJmjBU/c+fORbly5dTSQ4QVbXfu3IGRkRH8/Pzg7e2NkSNHfnWbzF/8s6KPw1kFSR2eSl0DVrbJVdeuXcXt7VOnTkWPHj0wadIkbN++HdWqVfvqm+LCML5ANkAVVrh++fIFM2bMkKsnWpDjS12jNb8aaAHShafA13Vgb968iXr16qFfv35YsWIFGjRooFLZDinCU0XvIyEhAbNnz1aqfERRaaKlzvA0qzqwR48eBZBRIsXf3x99+/aFiYkJmjVrJq7gVpYU4WlO92FsbAx7e3sxBBZWYCqqKDTQAqQLT4H/6sAK2wgDAgLQqlUrtG/fHgsWLED9+vVVavyXH+FpTvexatUqsUkdY+w/wrkvNDQUenp6Sq+2Z8XHp0+f8L///Q99+vRBp06d5K5bsmQJJk6cWEAzY0XJ9OnTcfny5YKeBivkAgICxJ25x48fh7m5OYCMPkYjRowAoL4FNExzcDirIKnDU6lqwObU5Epw9uxZrF69GqNHj4abmxsAxVccSD1+VoQAdejQoRg7diySk5PVukIir+NLXaM1PxpoAeoPT7OqA9ulSxe520RGRmLFihVYvXo1Ll68CEC1Y0Wd4SmALAMX2fvo2rUrpk2bhvDwcKXmW5SaaKkrPM2pDmzmlYmfP38Wb6fqOUBd4WlgYGC2tV1lV+k2adIENjY2Ss+3KDTQAtQfnmZXBzbzaoOdO3di79694gcmVWs1A9KGp7L30adPH9jb23/1N2asuMrueSucY/P6xREr+j59+oQ9e/agZs2a2Lx5s3j5hg0bxECEMUXk9j6CV+8zRXl4eGDixIl4/vw5WrRoIfYsYMUPh7MKkio8lbIGrCJNrmQJb26V6c4u5fg5SUlJgbOzM6ytreHv75/n8dQ5vlQ1WqUeX8rwNLc6sEL4k5mix4pU4emnT5/ElZQ53cft27fRoUMHpetMFbUmWuoIT1WpA6sIKcPTly9fQl9fH3fu3Ml2O75sCKzKdklNbKAFSBue5lYHVooareoMT7P7HYX7eP36Nbp3784rAVmxJTxHhNJYOclLiSVWvERFRWHv3r3o0qULJk+eDC8vLxgYGKiteSYrWoTz0OvXr/H8+XO591iZPxsI7/V4xSNTxrNnz1C1alU0adJErvkwK344nM2F1A20AGlrtCrS5ErV0FSK8SMjI5GYmKjQdtyUlBSltxxLOb7UNVqlHF/q8BTIuQ7smTNnVDrGpQxPY2NjMXHiREyePFkMCXO6j6ioKKXnDxSPJlqKkqoOrNTh6d69e2FiYgJ/f3/8+eef2R4LeXmzrmkNtID8CU+lrAMrdXgaHR0tN15W96HOhnGMaRLh+efo6Ahra2sMHz4crq6u4vNGluxrx8KFC3kFLctVeHg49u3bh0qVKsHExARXr14FwIEIy9r58+dhbGwMIyMjWFtbY8OGDV/dRjgPRUZGwsrKKstzFWNZCQoKQo0aNXDp0qWCngorYBzOKkCK8LQgarQW9iZaAQEBMDU1xaBBg9CjR49cVyELb6AUfSMl9fiAtDVapR5fivBUIEUdWKnD0+TkZJw9exYODg6YP3++GE5ldR9CeK1svUluopX9faizDqzU4WlaWhqaNGmCH3/8EdeuXct2LOGy+Ph4hX8HTW6gBWh2Ey2pwtOUlBS8fv0aVatWzbFpiOx5hRuBseLI2dkZzZs3x7Nnz9C2bVsYGxtjx44dcqGHcF6NioqCqakpPDw8Cmq6TMNERkZiz549ef4sxIq2R48eQV9fH8+ePcOnT59w8OBB2NrawsXFRbyNbDBrZmaGK1euFNR0WSEivIdXpLGXbO8YXnldfHE4mw2pwtOCrNFamJtoPX36FLq6ujh06BDu37+PGTNmYPbs2UhPT8/yQ6nw94mOjsaGDRtyXS0q9fiAdA2u8mN8TWuiBUgbngp//8uXL2PkyJFo3LgxlixZkmUILPuGbM2aNeJtssNNtJS7j7w20QKkDU8BICQkBIaGhmjRooVcvWDZ+5A9TgwMDPDkyROFx9fEBlqA5jbRkio8zfw6PnPmTEybNi3LLduyX4TMnz9fkhINjBVm4eHh6NevH/z8/ODs7IxWrVphwYIF0NPTw5YtW/D582fxtpGRkejcuTO8vb0LcMassFAmEJH9YpMDEQZkHD+yr9cXL16UKycXGRmJoUOHYtmyZQDk39+ZmpryeYgBkN/9MXDgQISHh2d7W+H9ZF4WWrCigcPZTKQMTwu6RmthbaKVnJyMCRMmiC9yQMaK5B49emR5e9kXwQ4dOuS69Vuq8aVucJWfDbRkf66wN9ECpA1PBW5ubtDR0cHZs2cxbtw4DBkyBPPmzZMLgWVX7HTo0AFeXl7ZjsdNtL4mdRMtgRThqTCXhIQEpKSkID4+HqGhoTA3N8ewYcPkxpUdW9kAQZMbaAGa1UQrP8LTGzduYMyYMUhKSsK1a9cwadIk+Pr6yo0pe14xMTGBp6en0r8LY5om8/MvNTUVwcHBCAgIgKGhoRjGGhgYYODAgXjz5g2AjBJjHTp0ELels+JNmUCEG8mxrAjHkLe3N9LS0uDr6wszMzM8ffpUDNF27dqFGTNmiLeNjo5Gx44dOZhlcs6fP4/mzZuLn9FTUlKyfK0D/gv9Q0JC8n2erPDgcFaG1OGp1DVgFbn/wtpE68WLF3j37p34Ifvhw4cwNTUVrxdW5gkvisL2NUVfBNU9vtQ1WvOjBqymNtESqDs8FaSlpSE5ORmDBw/Gtm3bAGT8PY4dO4aWLVti/vz5cgGvELgJKzKzwk20viZ1HVgpw1NhbFdXV1hZWWH8+PFYtWoVgIzdFpaWlhg4cKDcz0RGRsLExCTXsYtaAy1As5poSRWepqWlISUlBePHj4eWlhamTZuG48ePw8TEJMsvDHglICuu/P39ERgYiMDAQAAZ59RBgwYhMjIS9+/fR58+feQanp46dQp3794tqOmyQogDEaYq2WBWS0sLixYtAgDY2NjA0tISu3btwunTp1G/fn24u7uLP+fr64vbt28XxJRZIRUTE4PevXvj+fPniImJwenTp9G9e3ds3rxZ3J0nex6SrX3Nii8OZ2XkR3gqRQ3YotBEKzIyUu5yPz8/GBkZAQA8PT0xaNAgMTiIiIhA+/btcwzD8mN8KWu0Sjm+pjfRkiI8lSUE3zNnzsTy5cvlVlR07NgR/fv3Fx+TmJgYtG7dOtcAhZtofU3KOrBShqfC2G5ubmjevDlu3LiBESNGwMDAAElJSUhPT8f9+/fRtWtX8XyelJSE4cOH5/qmS9MbaAGa20RL6vA0LCwMQMZzxcLCAkOGDMGhQ4dgamoKLS0t7N69W7xtbGws2rRpw2/SWbHw7t077N+/HwDg7u6O3377Df3798cvv/yCo0ePIiIiAm3atIG9vT1q1KjxVSkxbuDEZHEgwvLKxcUFhoaGMDc3x5IlS8TLFyxYgDFjxqB///7ieShzCQRWvAnHgvDZsXv37mjZsiUGDhyIJUuWYMaMGbC2tpbLSvjLeCaLw9lMpG6glfk+8loDtig00fL19RUbWwkfrn19fTFkyBDcuXMHBgYGcqv39u/fr9Q2TynGl7pGq1TjF4UmWlKEp4KAgACsX78eycnJOHToEFq1aoXr168jLi4OL168QKdOnfDvv/8CyHiMzp49m+uKHW6ilTWp6sBKFZ7Kbm1PSEjA77//Dn9/f3h6eqJ169biFtvnz59/dXsACq/K0eQGWoDmNtGSKjxNTU3F+/fvUb16dSxbtgw+Pj7w9vbG5s2bERYWhmvXrqFVq1Zi2QogI6BSR7kHxgq7tLQ0HDp0CO3atcOSJUtgYWGBO3fuICUlBWfPnkXJkiVx9+5dPH/+HB4eHkp/CciKBw5EmLrcuXMHRkZG8PPzg7e3N0aOHPnVbRSpZcyKLx8fH8yePRvR0dGIiYnBokWLcO/ePQAZOxINDAzw4sULABnHkr6+Pp+HmIjD2SxI3UBL9j7yUgO2KDTRAoD79++jYsWKco+tv78/qlSpAn19fTE4VfWbSanGV3eN1vwYX1ObaAmkCE9ltzD17dsXderUwb59+wAAf/31F0xMTNCjRw80btz4q9qeigaT3ETra+quAytlePrp0yf88ccfYrkJAFi2bBl69+6Nli1birWhL168iN9//11ubGUCQk1toAVobhOt/ApPvby8sHDhQgwYMABmZmawt7cXz+FCoK3Kl5uMabpPnz7hf//7H/r06YNOnTrJXbdkyRJMnDixgGbGNAkHIkwdAgICxIVIx48fh7m5OYCML/xHjBgBgBvHsa8J79uuX78OGxsbaGlpwd7eXnx/BwBnzpyBvr6+XPPu6OjoXBe+seKFw9lsSN1AS7gPVWu0amoTLSDrD56rVq3Cn3/+KV4XHByM6tWrw8nJKcefy8/xpWxwlR/ja2ITLSB/wlM3NzcYGhrCyckJw4cPh7W1NXbs2AEgo17x/fv3xVWW3ESr8DXRkjI8TUtLQ3R0NF6+fIm3b9/i2LFjAIDdu3dDR0cHp0+fBpDxwbBJkyZwcXHJcbzcaFIDLaDoNNFSZ3gqXO/j44N9+/aJXxAlJibi6dOn6N69O7S0tGBkZITk5ORsa4gzVlx8+vQJe/bsQc2aNbF582bx8g0bNoiBCGOZcSDCpOTh4YGJEyfi+fPnaNGihfh+jzFBdHS0+F720qVLqFOnDm7cuIHLly+jRYsWmDhxIkJCQhAeHo5Ro0aJn1OzW+jGGIezOVA1PJW6BqxA05poyRK+4Y6Pj0dKSgquXLkCAwMDcet0UlKSuJJNlXBJneNL3eAqvxpoAZrVRCvzvKUIT4V59+vXDzt37hQv37JlC5o1a4Zdu3YpvY2cm2jlPLa668BKGZ6+f/8eq1evFhvTLFy4EFZWVnBzc0NycjLGjh0LCwsLdO7cGc2bN4ejo6PCY+dEkxpoAZrXREvK8FR4XF1cXNCoUSPMnz8ftWvXxsqVK8W/YVpaGnbs2MFbtBmTERUVhb1796JLly6YPHkyvLy8vio7xRjAgQjLH8+ePUPVqlXRpEkTcTEP72xhAj8/P/Tu3VtcQLFmzRps2rRJvP7Nmzf4+eefMXbsWMTExIif9fkYYjkpluGslOFpftRo1eQmWsL4V69ehaGhIQYMGIApU6YgJiYGtra2mDt3bq4/n5/jS12jNT8aaAljaloTLdl5qzM8lSWU3hg/fjz27Nkjd12HDh0wfPhwcRW4Is9RbqKV89jqrgMrdXh66dIldOvWDcuWLUNYWBg+fvyIpUuXwsbGBpcvX0Z6ejoCAwPh4+MjlktQtTxKZoW9gZbw85rWREuq8DQ+Pl78fx8fH+jq6uLVq1e4ePEiateuje7du2PZsmV4+fKl3M/xm3TG/hMeHo59+/ahUqVKco2a+HnCBByIsPwSFBSEGjVq4NKlSwU9FVbIJCYmwtLSEmvXrkVwcDDCwsKwYMECtG/fXu52CxcuRP369bFixYoCminTNMUunJUyPM2vGq2AZjbRysqlS5cwadIkNG3aFMbGxmjbtq1aC63ndXypG1zlRwMt2Z/VlCZaAnWHp7K3u3LlCg4cOAAA2LhxI2rUqCGeD548eQIzMzN0794dM2bMUGhcgJtoZSZ1E638CE+dnZ0xYMAALF68GB8+fMDnz5+xePFiDBs2DCdOnFBqrKxoagMtQLOaaEkZnoaHh8PBwUHcNvv06VM8fvwYly9fhqGhIWJiYrBlyxaUL18ey5cvl5sLY0xeZGQk9uzZo5YGvKxo4UCEqYPwuq7I58EPHz6I/8+1ZpkgOTkZK1euxJAhQ1C5cmUEBgYiMTERJiYmGDZsGJKTk+Hp6Yk+ffpg586dMDc3z1PjZ1Z8FKtwVsrwVOoasJlpahMtQeYXOG9vb6xZs0auBmxeqGN8qWu05kcNWEDzmmhJGZ4KLly4gIYNG+LKlSviZcuXL0eDBg1gbW2NevXqwd/fH8ePH8esWbMUfkPGTbT+k19NtKQITzOf17y8vNCvXz+5+5g7dy4GDx6M9+/fq3QfmtpAS/g5TWqiJXV4GhgYiPnz52PIkCFyK2zmzZuHdevWAQCuXr0KCwuLPJWPYEyTKROIyO484UCECTgQYXklnIccHR0xcOBAhIeHZ3tb4f1XdjvhWPG2fft2/Pjjjxg6dKi4WOHZs2do164dOnbsiMaNG+PevXtwcnJCz5495RZGMZadYhPO5kd4KmWNVk1toqXK/apz61Fex5eqRqvU42t6Ey2pwlMg44WzYcOGYlDk7e2N9evX4+PHj3j+/Dl8fHzw+vVruLm5QVdXV6kwhZto/Xe9lE20pAxPhbG9vLywcuVK7Ny5E2FhYbh+/ToGDBiApUuXIjQ0FJ8+fRJD67zMXVMbaAGa00RLyvBU9oPeyJEj0b17d/Gx/OOPP2Bra4v169fD0NBQ7nzGWHGiTCAinJv4gyzLCgciLK/Onz+P5s2b4+LFiwAyPpdnfo8i+3566NChue7kYsWD7HFy+fJl/O9//8OIESMwe/Zsuc9zwcHBiIiIgIeHB5o1a8Y7QZjCik04C0gXnkpdo1WgSU20vnz5Ir5pevr06VdbRfNK6vGlrtEq9fiAZjXRkiVleApkrAIdPXo01q9fj6lTp8LKygqWlpbo16+f+CY+MDAQHTt2VOjFlJtoyZO6DqzU4SmQsRq3efPm2L17NwwMDDB27FgAGV8a9OzZEwsXLszTBz5Na6AFaG4TrfwIT11cXGBgYIB58+ahTZs2GDhwIG7cuIHg4GBMnz4dAwcO/OpLMMaKGw5EmKo4EGHqEhMTg969e+P58+eIiYnB6dOn0b17d2zevFncySZ7HpKtfc0YkFHm8cKFC3j27BmAjN3G1tbWWLBgAe7fvy/e7u3bt5gxY0auJTQZk1Uswtn8CE+lrtGqSU20gIxwY/ny5di0aZNY2y87smGEoh/opR5fyhqtUo+vaU20MlN3eJpZbGwsJk2ahEGDBsHZ2RmJiYlwd3eHra2t+LulpKSIK/9ywk20vpZfdWClCk/fvn2Lnj174uPHj3BxcUGrVq3EVcTC7yf75ksZmthAS5g3oLlNtKQKT9PT0xEWFoZWrVqJwa6/vz8WLlwICwsL3Lt3D8B/jx83pGHFFQciLK84EGGqEl57hfeF3bt3R8uWLTFw4EAsWbIEM2bMgLW1tVyZMWXff7GiKygoSCyz5+Ligl9//RU9evSAkZERFixYgMjISNy+fRuWlpaYM2eO3GdsZcoQMgYUk3AWkD48lbpGq6zC3kQLyHiMW7VqhfLly2Pv3r3i5dmtkoiIiMCoUaMQERFR4ONL2eAqP8bXtCZamakzPM1MeO6npKSIx8bdu3dhaGio8gpObqL1NSmbaEkZngIZ2/EXLVqEpUuXomXLlggKCgKQsepLWFGsKk1qoAVofhOt/AhPU1JS0LdvX7i7u4vjXL58GbVq1UK/fv0QFhbGoSwrljgQYXnBgQhTJ2F3aHR0NGJiYrBo0SLxPUBgYCAMDAzw4sULABl1sfX19fk8xAAATk5OKFeuHNavX4+xY8eKnzGcnJzg4OCAf/75BwDg4eEh7oLj931MVcUmnFV3eFoQNVo1oYkW8N/v+Ndff6Fr166YMGEC7t27J64WFT5kCx9khTfjiobhUowvdY3W/BpfE5toyVJneKoIDw8PtGvXTgyIFHl+chOtrOVHEy1A/eFp5uM0MjISFhYW0NHREVevX7t2DfXr11d5JZemNdACik4TLXWHp5lvm5CQAFtbW8yaNQufP38GkPHeol+/fuLxw1hxxYEIUxUHIiyvhOPh+vXrsLGxgZaWFuzt7eUWd5w5cwb6+vriex0g4/0Sr7xmL168EHeHnTt3DrVq1fqqV8iyZctgZmZWQDNkRVGRDWfzIzyVugZsdgprEy3hNk+ePEFgYKAYmlhbW2PEiBEICgqCt7c3Vq5cKb4wRkZGwszMTKmah1KNL2WDq/wYX1ObaOVElfA0MjISiYmJ4irpnH4mPj4eAQEB4u1yG5+baOU8thR1YKUKT2VX2bi7u2PmzJlYvXo1AgICcP/+fTRp0gQTJkzArFmzoKOjI7ezQlWa0kAL0NwmWlKGp7Jfgq1Zswbnz59HcnIy3r59i9atW2PAgAFwcHBAo0aN1HK8MKaJOBBhecGBCMur6Oho8T3epUuXUKdOHdy4cQOXL19GixYtMHHiRISEhCA8PByjRo0SF8Wkp6crtdCBFW0XL16ElpaWmDe4urqiZs2a2LZtm7ggwdvbG1ZWVnkqF8iYrCIbzgLShqdS1IDV9CZaQMaHbUNDQ4wePRpt27bFrVu3EB4ejr59+8La2hpVq1YVw/C4uDh07NhRqUBFivGlrtEq9fiAZjXRkjI8DQgIgKmpKQYNGoQePXrk+EFP9g2YIs9/bqKVM3XXgZUyPP38+TMmTJgAFxcXXL9+HU2aNMHcuXMxbtw41KpVCw8ePIC/vz927tyJrVu3Kt2ECtDcBlqyc9e0Jlr5EZ5euHABjRs3xvbt21GtWjVMnjwZ79+/x+fPn3H8+HFs3ryZa2WyYokDEaYOHIiwvPDz80Pv3r3x+vVrAMCaNWuwadMm8fo3b97g559/xtixYxETEyM2A+dV1ywrLi4uqFSpkvj57MyZM6hduzYGDx6MLVu2QEdHB2fOnCngWbKipMiGs1I30JKlrhqwmt5Ey8fHB23atEFERAQ2bNiANm3aIDQ0FEBGyObj4yMX3N29e1fum/CCGl/qBldSjw9oThMtKcPTp0+fQldXF4cOHcL9+/cxY8YMzJ49O9sPfsIxHh0djQ0bNoi/R1a4iVbO1F0HVurw9PXr15g2bRpGjx4NCwsL8efT0tKwa9cutGvXTlxlqQpNb6AlzF0Tm2hJGZ76+flBT08PL168wOXLl9GwYUNYWFgcq1O+AAApDklEQVRgzJgx4tZsxoojDkSYOnEgwlSRmJgIS0tLrF27FsHBwQgLC8OCBQvQvn17udstXLgQ9evXx4oVKwpopkyTODk5yZ2PXFxcUKFCBdjY2Ii73/i1jKlLkQ1nZUnVQEvdNWA1uYkWkLHK6vDhwzhx4gSMjIzEgMDd3V1uG7iq1Dm+1DVa87OBFqAZTbSkDE+Tk5MxYcIELFu2TLxs37596NGjR5a3l+0K3aFDBzEczw430cqZuuvASh2eAhlfVvzxxx+oWbOm3HHz8uVL9OrVS9xloAxNb6AlzEVTm2hJHZ6+f/8eAQEB8Pb2RvPmzcXzbJkyZTBr1ixERkbm+T4Y0zQciDApcCDClJWcnIyVK1diyJAhqFy5MgIDA5GYmAgTExMMGzYMycnJ8PT0RJ8+fbBz506Ym5uLXxQxlhPhfCT7+UaZxvGMKapIh7NSN9ASqKMGrCY30RI67Z44cQItWrRAp06dxKZI7u7ucg0flCH1+FI3uMqPBlqAZjTRkjo8BTLqlL179058PB4+fAhTU1PxeuENmHDMR0VFwdTUVOHmI9xE6z/50URLivA0s3fv3mHevHkYNGgQTp06BQDw9fWFjo6O+IFQUUWlgRaguU208is83bx5MxYsWAAAuHPnDiwtLblWJiu2OBBhUuFAhClr+/bt+PHHHzF06FDxfeKzZ8/Qrl07dOzYEY0bN8a9e/fg5OSEnj17qrxjjBU/Li4uKFWqlFzmwF8QMXUr0uGsQNXwND9qtGpqEy1hXGdnZ4wZMwZhYWFISkpCx44d0b17d9y5cwfnz59Hs2bNVAoIpR5f6hqtBdVACyh8TbQEUoWnwv1nDl78/PxgZGQEAPD09MSgQYPE+mQRERFo3749rl27ptDYAm6ilb9NtNQZnsrWgN2/fz/u3buH9PR0vH//HgsWLEDjxo1hZWUFExMTuSY1itLUBlqy4ws0vYmW1OHp+vXrYWFhgT///BNNmzblsIAVexyIMKlwIMJyI3s8XL58Gf/73/8wYsQIzJ49W668XXBwMCIiIuDh4YFmzZqpVOaNFW+Ojo5y7/EZUzeNDmelDk+lrtEq0MQmWkDGGyY9PT25VY3JyckYOXIkrK2tMXDgQFy4cAGA6jUPpRpf6hqt6h5fU5toyd5OivBU4Ovri3nz5snN0dfXF0OGDMGdO3dgYGAgF/zs378/10CFm2jJk7oOrJThaU41YMPDwxEeHo7p06ejb9++YuMuVVaGaloDLdm5F6UmWlKHp5GRkVi+fDmmTp2q9p04jGkKDkRYfuFAhOXG19cXFy5cwLNnzwAA9+/fh7W1NRYsWCBXtuvt27eYMWMG73ZhecJfEDGpaHQ4K3V4KnWNVkBzm2jFx8ejW7duuHjxIr58+YKjR49iwIABmD9/vnibvNT1lXp8KWu0qnt8TW2iJUuK8FTW/fv3UbFiRVy/fl28zN/fH1WqVIG+vr44NjfRKpxNtKQKTxWpAbt06VJERkYiNDRU5ZBdmLsmNtACil4TLVXDU0W+BMvqvKjMDgLGihIORFh+4vMsEwQFBYk9PVxcXPDrr7+iR48eMDIywoIFCxAZGYnbt2/D0tISc+bMkVu8kJCQUFDTZoyxHGl0OCtleCp1DViBJjXRkvXlyxfY2dnBzs4O3bp1w6xZs7B161ZYWlqK95GXN1FSji91jVZ1jq/JTbRkqTs8zep2q1atwp9//ileFxwcjOrVq8uFM8ocM9xE62vqrgMrZXiqaA3YcuXKYdmyZSqH7JrcQAvQrCZaUoanynwJBnxdOoSx4oADEcZYYeDk5IRy5cph/fr1GDt2rPh+3MnJCQ4ODvjnn38AZJR68/X1BcDhPmOs8NPYcFaq8DS/asBqahMtWV5eXti2bZu4ku3evXvQ19eXC7AK8/gCVWq05sf4mtxEKz/CUx8fH8yePRvx8fFISUnBlStXYGBggKioKABAUlISQkJClB5XwE20sqauOrBSh6fcQEsxmtJES8rwNL92EDCm6TgQYYwVpBcvXog7Zc+dO4datWrBwMBAbtfnsmXLYGZmVkAzZIwx1WlcOCt1eApIV6NV05to5eTy5cvQ09NT+7iqji91g6v8aKAFaGYTLYGU4Wl6ejquXr0KQ0NDDBgwAFOmTEFMTAxsbW0xd+5cpcYScBOtr0lZB5YbaOU8dn420AIKdxOtorKDgDFNxYEIY6wwuHjxIrS0tMTP/66urqhZsya2bdsmLszy9vaGlZWV3PtvxhjTBBoXzgLSNtCSugasJjfRys7bt29hb28PFxcXtY6r6vhSN7iSenzZ22paEy3Z+as7PM3OpUuXMGnSJDRt2hTGxsZo27atUvWIuYlW1qRsosUNtHKW3w20gMLbREuTdxAwVlRwIMIYKyxcXFxQqVIlccHBmTNnULt2bQwePBhbtmyBjo4Ozpw5U8CzZIwx5WlcOCt1eCplDVhNb6KVE9k6vlLU4lN0fKm3p+bn9ldNbaKVWV7D0+xkPg68vb2xZs0apYIfbqL1tfxqosUNtLJWEA20gMLdREuTdxAwVlRwIMIYKyycnJzkzkcuLi6oUKECbGxs8PDhQwBcUoUxpnk0LpxVd3ianzVaNamJlnA7Pz8/eHh44O3bt0hMTMzytkJIpkxNSCnGl3qFVX5vf9XEJlqy1BGeKiKr+Sk6Z26iJS8/mmhxA62cqbsGrCY30dL0HQSMFTUciDDGCgvhfCTbE4JfnxljmqzQh7NShqcFUaNVk5poubi4oGnTphg7dizq16+f5YoI2QByxIgR+PDhQ4GOL/X2VCnHLwpNtLKianj65csXMbh8+vSp+AWDunETrf/kVxMtbqCVO3XUgC0KTbSKyg4CxooKDkQYY4WFi4sLSpUqJZcB8Os1Y0xTFepwNj/C04Ko0SoobE20ZN2/fx/GxsYIDg6Gk5MT9PT0xIBcIBucduzYUan6e+oeX+oVVvm1/VWTmmhJHZ46Oztj+fLl2LRpk7g6NDvCsZKamioG48rgJlrS1oHlBlrKy2sN2KLSREvTdxAwVhRxIMIYKywcHR3l3hMzxpimKtThLCBteFqQNVoLWxOtzB48eIDDhw/jzJkzMDIyktvCJruqLSoqCp07d1a6MYoU40u9wkrq8TWpiRYgfXialpaGVq1aoXz58ti7d694eeZjWRg7IiICo0aNkqtPnBVuopU9KerAcgMt1ahaAxbQ7CZaRXUHAWNFDQcijLHChF+vGWOarlCHs1KHp1LXgM1NYWmiJevRo0cICAjArVu3UL58eTRt2lRcQXXt2jU0bNgQvr6+ADK63Ovr6yu1MlTK8aXenpqf218LcxMtgVThqewYf/31F7p27YoJEybg3r17YtAj1F0VgqHIyEh07tw514CMm2hlTeo6sNxAS/7npG6gBWh2Ey1N2kHAWHHHgQhjjDHGWN4V6nA2P8JTddZo1cQmWrJiYmIwevRoHDt2DACwaNEi/Prrr3B1dcXff/8NPT09uRDiw4cPePDgQYGNL/UKq4LY/qoJTbRkb6vu8FQY98mTJwgMDBRr2llbW2PEiBEICgqCt7c3Vq5ciejoaHFsMzOzHEMlbqKVO6maaHEDrf9I3UBLmAeguU20NG0HAWOMMcYYY4zlVaEOZwHpG2jJUkcNWE1ropU5UFiyZAmMjIzED9xr165Fz5494eDgADc3N/FnsgonCmJ8qbenFtT218LaREuq8FSWo6MjDA0NMXr0aLRt2xa3bt1CeHg4+vbtC2tra1StWlUMq+Pi4tCxY8ccV11yE62s5VcTLW6glSG/GmgBRaeJlibsIGCMMcYYY4yxvCr04awsKRtoqaMGrCY10ZKt+5m5G32/fv1w+PBh8d+KBqX5OT4g/QorKcYvCk201B2eyvLx8UGbNm0QERGBDRs2oE2bNggNDQWQsfrVx8cHjx49Em9/9+5dPHnyJMcxuYlW9mPnZxMtbqCVPw20AM1voqUpOwgYY4wxxhhjTB00JpyVuoEWkPcasJrSROvdu3fYs2cP4uLiEBoaiooVK2LevHli3dBly5Zh5syZ4u2VfaylHj8rUm9PVdf4mtpESyBFeCrL0dERhw8fxokTJ2BkZCSG1+7u7vj48aPC4wi4iVb28ruJFjfQkqYGLFB0m2gV1h0EjDHGGGOMMaZOGhPOAsqHp1LXaBVoWhMtb29v+Pn5ISwsDNHR0Xjw4AF27NgBIyMj2NnZYf369ShVqhS8vLyUfizyY3xZUq+wUvf4mthES5ZU4amwAvzEiRNo0aIFOnXqhDdv3ohjGxgYqFyflJtofa0gmmhxA61IucvVWQMW0KwmWkVhBwFjjDHGGGOMqUuBhrP5EZ5KXQNW05poCeLj49G3b1/MmTNHXPmYkpKC+fPnY+bMmdDS0sKdO3cUnmd+j5+Z1NtT1TG+pjXRkh1bivBUGNvZ2RljxoxBWFgYkpKS0LFjR3Tv3h137tzB+fPn0axZM5VKmXATreypsw4sN9DKndQ1YDWtiZam7yBgjDHGGGOMMXUq8JWzUoanUtWA1cQmWjExMWIYdenSJfj5+eHcuXOwsbHBkiVLEBAQIHd7oWSCoqQaX+oVVvmx/VUTm2jJzluq8BTIeP7r6enJbQtPTk7GyJEjYW1tjYEDB+LChQty81EGN9HKWV7rwHIDLcXkVwMtQDOaaGn6DgLGGGOMMcYYU6cCDWelbqCl7hqwmtxE6927d2jcuDHs7e3RoEEDMQxzcnKCtbU1li9fLhcmCY+NomGBVONLvcIqv7a/aloTLYGU4Wl8fDy6deuGixcv4suXLzh69CgGDBiA+fPni7dRNlTiJlrKyUsdWG6glb38bqAFaE4TLU3cQcAYY4wxxhhjUirQcFaqBlpS1IDV5CZawm23bduGkiVLYsKECQD++/Dr5OSEXr16YfHixeIHY2VIOb7U21PzY/urpjXREkgRnsr68uUL7OzsYGdnh27dumHWrFnYunUrLC0txd9BleOcm2gpTtU6sNxAK3cF0UAru7EKQxMtTd1BwBhjjDHGGGNSK5BwVsoGWlLVaNXUJlrCB+L379/j3LlzOHDgAEqVKoU1a9bI3e7ixYtK1cLNj/Gl3p6aX9tfNbGJFqD+8DQrXl5e2LZtG+7evQsAuHfvHvT19fH27VuVxuMmWv/9nNRNtLiBVs6/g7prwBaFJlqauoOAMcYYY4wxxqSU7+GsFOGp1DVgBZrWREt4TJycnNCxY0cxQLp69SpKlSqFrVu3wsvLC23btlWphqVU40u9wiq/xtfEJlqZqTs8zcnly5ehp6en8ry5iVYGqZtocQMt5airBqymN9HS1B0EjDHGGGOMMSa1fAlnpQpPpa4Bq6lNtGR5eHhAV1dXDC6Ex+HatWto1KgR2rZtixMnTig9rtTjS709Varxi0ITrezkNTzNydu3b2Fvbw8XFxcAqs2bm2jlXxMtbqCVO3XXgNX0JlqauoOAMcYYY4wxxqQmaTgrdYMrKWvACvehiU20ZK1ZswYHDhwAkLFNNzU1VfzwHh4ejvfv3ys9ptTjS73CSurxNa2JliLUEZ7mRjZEUnY1pyxuoiV9Ey1uoKW4vDbQkr29JjXRKko7CBhjjDHGGGNMSpKFs1KHp1LXgNXUJlqZH8f58+ejW7ducpd5e3vj9OnTSs85P8YHpN+eKuX4mtZESxnKhqfCPPz8/ODh4YG3b98iMTExy9sK4yUlJeV5ntxES7o6sLK4gZa0NWBl56FpTbSK8g4CxhhjjDHGGFM3ycJZqcNTQLoasJraREsY19PTExs2bMCBAwfg7u6OKVOmYPXq1UhNTcWtW7fQqFEjXLp0SeV5q3t8qben5uf2V01qopUf4amLiwuaNm2KsWPHon79+jhz5ky2Y0dGRmLEiBH48OGDUveRGTfR+o8668ByAy15+dFAC9DcJlpFcQcBY4wxxhhjjElB0rIG6g5P86NGq6Y20RIIH4g3bdqEdu3awczMDCtXroStrS2MjIxgZGSUp2276h5f6hVWBbH9VZOaaEkZnt6/fx/GxsYIDg6Gk5MT9PT0xIA8q7E7duyYYwjJTbRyJmUdWG6g9TWpG2gBmttEqyjvIGCMMcYYY4wxdVNrOCt1eJofNVoBzW2idffuXRgbG+P9+/c4deoUDAwMMGLECHTv3h0hISGIjY3N07ZgqcaXentqQW5/LcxNtNQdnmb24MEDHD58GGfOnIGRkZH4fHdxcUFYWJg436ioKHTu3DnHsbmJVu7yq4kWN9DKnwZagOY20dKkHQSMMcYYY4wxVtDUGs5KGZ5KXQNWliY20QKAkJAQ+Pj44MqVK9DX10dgYCAcHR1RqVIldO7cWenttPkxvtQrrApy+2thb6KlzvBU1qNHjxAQEIBbt26hfPnyaNq0qRhgXrt2DQ0bNoSvry8AIDY2Fvr6+jmuDuUmWlnL7yZa3EBL+hqwRamJlibtIGCMMcYYY4yxgqS2cFbK8DS/asAKNLGJlqy//voLS5YsAZDxQX7WrFl4+PChWsZW9/hSr7Aq6O2vhbGJlrrDU1kxMTEYPXo0jh07BgBYtGgRfv31V7i6uuLvv/+Gnp6eXJj34cOHHJ+z3EQrZwXRRIsbaElTA7YoN9EqzDsIGGOMMcYYY6ygqSWclTI8za8asJrWRCsnR44cQa1atbB48WLUq1cPFy9eVMu4Uo0v9QordY6v6U201B2eAl/XdF2yZAmMjIzEsHPt2rXo2bMnHBwc4ObmJv5MVqtes8JNtLKfvzrrwHIDrdxJXQO2KDbRKuw7CBhjjDHGGGOsoOU5nJU6PAWkrwGraU20chMXF4f9+/fD3t5eDMPUScrxpd6eqo7xNamJFiBdeCobmgmhnqBfv344fPiw+G9Fg9jM8wa4iZYi1FEHlhto5U6qBlrC/IpqE63CuIOAMcYYY4wxxgoLtayclTo8lbIGrKY20VKE8HeQ6gO3useXeoWVOsbXpCZaUoan7969w549exAXF4fQ0FBUrFgR8+bNE0O3ZcuWYebMmeLtVf1bchOtnKmzDiw30Mp+3lI30AI0q4mWpu8gYIwxxhhjjLHCRC3hrLrD0/ys0aqJTbQUJXXtPU3cnprX8TWliZbU4am3tzf8/PwQFhaG6OhoPHjwADt27ICRkRHs7Oywfv16lCpVCl5eXkqNmxk30VJMXptocQOt7MfOrwZagGY10dK0HQSMMcYYY4wxVlipFM5KGZ4WRI1WQLOaaGkSqVdY5df2V01qogXkT3gaHx+Pvn37Ys6cOeLW8ZSUFMyfPx8zZ86ElpYW7ty5o9SY3ERLntR1YLmBVs4KqoEWULibaGnSDgLGGGOMMcYYK+yUDmfzIzzN7xqtgOY10dIkUm9PlXp8TWyiBag/PI2JicG9e/cAZGzT9/Pzw7lz52BjY4MlS5YgICBA7vZCoKIsbqL1n/xoosUNtLJWUA20gMLfREtTdhAwxhhjjDHGmCZQaeWslOFpQdVo1eQmWoWZ1CuspBpfE5toSR2evnv3Do0bN4a9vT0aNGggBmJOTk6wtrbG8uXL8fTpU/H2wuOn7PZ6bqL1H6mbaHEDrewVZAMtoHA20dK0HQSMMcYYY4wxpgmUDmelDk8LskYroHlNtAo7qbenqnt8TW6iJWV4Ktxm27ZtKFmyJCZMmADgv8fAyckJvXr1wuLFi8X6pHnFTbSkb6LFDbRyps4asJreREtTdxAwxhhjjDHGWGGndDibX+FpQdVo1cQmWoWR1NtTpRhfk5toSRmeCmO/f/8e586dw4EDB1CqVCmsWbNG7nYXL15Uy0o2bqIlXR1YbqClOnXUgNW0JlqauIOAMcYYY4wxxjSNSmUNAOnD0+Jco1XTSb09VarxNb2JlhThqTC2k5MTOnbsKAZ2V69eRalSpbB161Z4eXmhbdu2iIyMVGpsRe87t8sy4yZaOc+bG2gpTx01YDWpiZYm7yBgjDHGGGOMMU2jcjgrdXhaXGu0aiqpt6fm1/ZXTWuilR/hqYeHB3R1dcUVn8Jjeu3aNTRq1Aht27bFiRMnFB6Pm2jlTMo6sNxAS3V5aaAFaE4TLU3eQcAYY4wxxhhjmkjlcDa/wtPiVqNV00i9PVXq8YtCEy11h6eZrVmzBgcOHACQUQMzNTVVDKfCw8Px/v17pebMTbRyJlUTLW6gJS8/asACmtdES1N3EDDGGGOMMcaYplI5nBVIHZ5yKFt45UeDK6m3vxaFJlrqDk8z327+/Pno1q2b3GXe3t44ffq0SvPlJlpZz1nqJlrcQOtrUtaABTS3iZam7SBgjDHGGGOMMU2W53CWw9PiS+oVVlKPr6lNtKQMT4WxPT09sWHDBhw4cADu7u6YMmUKVq9ejdTUVNy6dQuNGjXCpUuXVB6fm2jJj51fTbS4gdZ/pKoBq4lNtIrCDgLGGGOMMcYY01R5DmdZ8Sb19lSpxtfUJlpSh6fAf3VJN23ahHbt2sHMzAwrV66Era0tjIyMYGRkJLfaT5l5cxOtrxVUEy1uoKXeGrCa3ESrKOwgYIwxxhhjjDFNxeEsU4rUK6zyY/urJjbRkiVFeCq4e/cujI2N8f79e5w6dQoGBgYYMWIEunfvjpCQEMTGxiIkJASA8gEQN9H6WkE10eIGWuqtAavJTbQ0dQcBY4wxxhhjjBUVHM4ypUi9PTW/tr9qWhMtgZThKQCEhITAx8cHV65cgb6+PgIDA+Ho6IhKlSqhc+fOCjflyoybaGWtIJtocQMt9dSABTS3iZam7iBgjDHGGGOMsaKEw1mmMKm3p+bn9ldNa6IlkCo8zeyvv/7CkiVLAGQEnLNmzcLDhw9VHo+baGVPXXVguYFW1vKjgRageU20NH0HAWOMMcYYY4wVFRzOMoVIvT01v8YXaFITrayoOzzN7MiRI6hVqxYWL16MevXq4eLFi0r9PDfRUk1e68ByA63/SFkDFigaTbQ0dQcBY4wxxhhjjBUlHM6yXEm9wiq/xte0Jlo5yWt4mpu4uDjs378f9vb2YiCmKG6ipZq81oHlBlr/kboGrHAfmt5ES1N3EDDGGGOMMcZYUcLhLFOI1CuspB5fU5toZScv4akyhL+DsvPmJlqqyUsTLW6g9R+pa8BqahOtoraDgDHGGGOMMcaKAi0AIMZysXbtWqpcuTLZ2dlRcnIylSxZkoiISpYsSREREZSUlETVq1cnAKSlpVWoxr937x5NnDiRzp49Szdv3qQVK1aQvr4+hYaG0t69e6l8+fIUExND2traKo3//v17Cg4OpsTERJoyZQqdOnWK/P39afjw4aSvr09ubm70zTffKDWmotLT06lEiRIqP+65UWXchIQE6tevH02dOpU6dOhA586dozNnzlCDBg1o2bJlREQUHx9P33//vUpzSkxMpLFjxxIRUVhYGDVr1oxq1apF7u7utGHDBqpTp45kj0dkZCRVrFiRiIjS0tLE4zQ7wjyePHlCHz9+pPr161OVKlXo22+//eq2wnjJyclUunRphef0+PFjKl26NEVFRZG5uTnVqlWLHjx4QKVKlaLr16/TqFGj6NixY6Svr09xcXHUvn172rJlC7Vr1y7XsWNjY2nGjBlkampK1tbWtHjxYjpw4ADt3LmTPn78SOvXr6dly5aRhYUFEWX8Pd6/f0/NmzfPcdxr165RxYoVqUqVKvTdd99RUFAQ3b59m/bv3086Ojqkp6dHs2fPpkuXLpGJiYnCj4WshIQEGjp0KDVo0IB+//13qlatGqWmptLixYspOTmZ1q5dS7dv3yYjIyOlxhX+pqGhoXTnzh2KiooiBwcHWrFiBc2YMUO83aVLl6hy5cq5PhZZje3s7Exr166lvXv3Ur169cjb25vMzMxow4YN1KRJE1qwYAE5OztThQoVlB7by8uLHj16RBUrVqQaNWqQi4sLaWtr07Rp0+ju3bs0fPhw2rx5M3Xp0kWZh4WIiC5cuEDz5s2jESNG0MmTJ+m7776jzp0705MnT+j58+dERLRw4ULxeGGMMcYYY4wxRsQrZ1mWpN6emp/bXzW1iZYiCmOtRm6iJU/KOrDcQOs/UteABTS/iVZR20HAGGOMMcYYY0UBh7PsK1JvTy2o7a+FvYlWUcJNtDJIUQeWG2hlLT8aaAGa20RL6vA0v74EY4wxxhhjjLGihsNZliWpG1zldwMtoHA30SrKuImWeurAcgOt7OVXAy1Ac5toFeUdBIwxxhhjjDGmyaQphMk02r1792jJkiXk6upKN2/epISEBGrQoAF5e3urpUar1ONnp3fv3pSYmEje3t60detW6tq1q1rGFXz//fc0fPhwsrOzk7QOrCYJDg6mQ4cO0YoVK6hHjx5qf0xq1qxJ69evF+vApqen51oHVpaWlhZNnjyZ7t69SytXrqTTp0/Tzz//TBcuXKAWLVpQ5cqVqWTJkhQdHU39+/enpUuXUvv27XMdV6gDm5SUROPGjfuqDuy0adPo2LFjVKVKFYqLiyMTE5Mc68CGhISQq6sr2djYUGxsLOno6NC4cePot99+o5EjR1Lz5s3p4cOHNHjwYPH3UsbLly+pVatWFB8fT2XLliUPDw+6ffs27dq1i65du0Z6enr0559/Uo8ePcjExETp8QFQjRo1aOLEiTR58mQaO3YstW7dmtLT08X6o7t376aUlBSaOXMmlS1bVryP3O4LMjVga9SoQfv27SMHBweqXbu2WAPWwsKCSpcuTZUrV6ayZcsqPXfZOURFRZGHhwfZ2dmJtYGvXbtGnz59or59+4q3U+QxQhZ1YE1MTCg+Pp7WrFkj1oEdPXo0bd68Wal5Z6atrU3a2tq0atUq6tu3L9WtW5devXpFI0eOpMGDB6utLnatWrVozpw5lJ6eTocOHaJt27aRnp6eWsZmjDHGGGOMsaKIG4Kxr0jd4KogG2gRFc4mWkUZN9HKexMtbqCV89hSNNCSHb8oNdE6evQozZkzh0aMGCGGp+r8oio+Pp5OnDhB3t7eNGjQIDI3N1fb2IwxxhhjjDFWFHE4y7K1atUqSk5Opj/++IMuX75M7u7uNHjwYLWtgpJ6/OxweJo3+RGeXrhwgWbPnk3t2rUjDw8PWrVqFVlZWWU5dlRUFE2fPp1WrFhBVatWzXVsKcJT4TQqHFdLly4lJycn8vHxoRIlStC6devIy8uLtLW1qV+/fmRubk7IKCtDJUqUUOgxUXd4GhsbS8+fPydDQ0Nyd3en6tWrU1BQEB0/fpwaN25MAwYMoEaNGom3DwoKorp16yo0tuzjImV4SkTk6elJU6ZMoU2bNlGnTp3EL1+uX79ODg4OVKlSJfr9999pwIABSo9NJG14eu/ePZo4cSKdPXuWbt68SStWrCB9fX0KDQ2VbBdBfoWnUn8JxhhjjDHGGGNFRn7VT2CaR+oardxAS3NxE60MUtaB5QZaipGqgRZQtJtoCX8HVeatCKnGZYwxxhhjjLGihlfOsmxJvcKKt79qpgcPHtD48ePp1KlT9PDhQ1qwYAFdvnyZfv75Z/E2sqta+/Tpo3CtViIiX19fCggIoDJlytDKlSvp6NGjVLduXbk6sFpaWhQdHU39+vWjRYsWZTt2amqqWCLj48ePVKVKFfG6/v37U9++fcU6rcJKP0XlVgd2+fLlFB0dTatXryYi5Vdsh4SEUJcuXcjY2Jhu3rxJBw4coNatW5OzszMdOnSImjVrRv369aOGDRvKja/I/Qi32b59u1gDduvWreJj4OzsTLt37yZDQ0OxBqyq1q5dS5UrVyY7OztKTk4Wy1qULFmSIiIiKCkpiapXr67w45P5dgsWLKD79++Tq6ureFlWNWBVkV8lWApiF4GyxyNjjDHGGGOMMWlwOMtyJfX2VN7+qlnUGZ7KUncdWKnDUynrwEoZnkLCGrCy4wvUGZ4iH2rAZkXq8FTqOrCMMcYYY4wxxgov6bousSJD0a7phXV8ph5CeJqUlETjxo37KjydNm0aHTt2jKpUqUJxcXFkYmKiVBOtbdu2iXVgp06dSgcOHCAPDw+xDuzatWtJX1+fiDJWXe/fvz/H4PDly5fUqlUrio+Pp7Jly5KHhwfdvn2bdu3aRdeuXSM9PT36888/qUePHmRiYqL08de+ffuv6sCOHTuWRo0aRYsXL6bQ0FBKTU2l77//XqlxZcPTGjVq0L59+8jBwYFq164thqcWFhZUunRpqly5skrBbOYasHXq1CEzMzMqU6aM2htomZiYUHx8PK1Zs0YMT0ePHk2bN29WamyijHOEbA3YvXv3ijVgHz16RK1btyYiojVr1qgtmCUiqlWrFs2ZM4fS09PF8FSdq1p79+5NiYmJ5O3tTVu3buVgljHGGGOMMcaKEV45yxjLFTfR+u9xkLKJVnbhKTfQylAQDbSIuIkWY4wxxhhjjDHpcDjLGMuSVOGplHVgpQ5PpawDK5A6PFV3DVhZUoen+VUDNjtSh6ccyjLGGGOMMcZY8cPhLGNMDjfRylp+NdHiBlq5K4gGWkQcnjLGGGOMMcYYUz/FUxXGWJEXEhJCBw4coPj4ePrw4QM1atSI5s+fT/v27SMioubNm9PDhw/F2ysbVGVVB7ZmzZq0a9cusre3p7Jly9LGjRvpypUrSo8PgGrUqEETJ06kf/75h7p06UKtW7em9PR0srCwoCFDhpCPjw8dO3aMEhIS5MZXNJiVrQO7e/duWrt2rRhOW1hY0MSJE6l3795K14GVFRUVRceOHSMiotKlS1PJkiXp5s2bdObMGfrpp5+oevXqCs1Zdt5eXl60ceNG+vvvv8nExIQaNWpEa9asobS0NPLx8aHRo0dTuXLlFJ5zdrS1tcnY2Jh8fHyob9++VLduXSpTpgyNHDmS1q1bp7ZVrbVq1aK9e/fSn3/+SePGjaPOnTtLHswScV1sxhhjjDHGGGPqxw3BGGMibqKV/dhSNNHiBlqq4QZajDHGGGOMMcaKCi5rwBiTw020viZlHVhuoKU6bqDFGGOMMcYYY0zTcTjLWDHHTbRyJ1UTLW6glTccyjLGGGOMMcYY03QczjJWzHETreznLZCqiRY30GKMMcYYY4wxxoo3rjnLWDEm20RLCE9lm2gREe3evZtSUlLE8DQvTbTUXQdWEBUVRR4eHmRnZ0elS5cmoqzDU2WbaElRB1aWtrY2aWtr06pVq8QGWq9evaKRI0fS4MGD1dpAS8oasNnhYJYxxhhjjDHGGMsZh7OMFVPcRCtrBdFEixtoMcYYY4wxxhhjxROXNWCsGOImWtkriCZa3ECLMcYYY4wxxhgrnjicZayY4iZaWSvIJlrcQIsxxhhjjDHGGCteOJxlrJjiJlo5K4gmWhyeMsYYY4wxxhhjxUuJgp4AYyx/ZP4eJioqio4dO0ZERKVLl6aSJUvSzZs36cyZM/TTTz9R9erViUj5JlobN26kv//+m0xMTKhRo0a0Zs0aSktLIx8fHxo9ejSVK1cuT7+HtrY2GRsbk4+Pj9hEq0yZMjRy5Ehat26dWpto7d27l/78808aN24cde7cWfImWhzMMsYYY4wxxhhjxQs3BGOsGOAmWsrjJlqMMcYYY4wxxhiTGpc1YKyY4CZaquEmWowxxhhjjDHGGJMKh7OMFQPcREt1HMoyxhhjjDHGGGNMKhzOMlYMcBMtxhhjjDHGGGOMscKHa84yVgxoa2uTtrY2rVq1Smyi9erVKxo5ciQNHjxYrU20pKwDmxUOZhljjDHGGGOMMaapOJxlrBjhJlqMMcYYY4wxxhhjhQeXNWCsGOEmWowxxhhjjDHGGGOFB4ezjBVD3ESLMcYYY4wxxhhjrOCVKOgJMMbynxCcShWgcjDLGGOMMcYYY4wxljsOZxkrhjg8ZYwxxhhjjDHGGCt4HM4yxhhjjDHGGGOMMcZYAeBwljHGGGOMMcYYY4wxxgoAh7OMMcYYY4wxxhhjjDFWADicZYwxxhhjjDHGGGOMsQLA4SxjjDHGGGOMMcYYY4wVAA5nGWOMMcYYY4wxxhhjrABwOMsYY4wxxhhjjDHGGGMFgMNZxhhjjDHGGGOMMcYYKwAczjLGGGOMMcYYY4wxxlgB+D+CS2hMkHyT0QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization: Steps comparison\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
    "\n",
    "categories = df[\"category\"].unique()\n",
    "colors = plt.cm.tab10.colors\n",
    "\n",
    "for ax, category in zip(axes.flat, categories):\n",
    "    cat_df = df[df[\"category\"] == category]\n",
    "    cat_df_valid = cat_df[cat_df[\"steps\"] > 0]\n",
    "\n",
    "    if len(cat_df_valid) > 0:\n",
    "        bars = ax.bar(range(len(cat_df_valid)), cat_df_valid[\"steps\"].values)\n",
    "        ax.set_xticks(range(len(cat_df_valid)))\n",
    "        ax.set_xticklabels(cat_df_valid[\"name\"].values, rotation=45, ha=\"right\", fontsize=8)\n",
    "\n",
    "        # Color by convergence\n",
    "        for bar, converged in zip(bars, cat_df_valid[\"converged\"].values):\n",
    "            bar.set_color(\"green\" if converged else \"red\")\n",
    "\n",
    "    ax.set_title(category)\n",
    "    ax.set_ylabel(\"Steps\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.suptitle(\"Steps to Convergence by Solver (Green=Converged, Red=Not Converged)\", y=1.02)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f8a5022f990>,\n",
       "  <matplotlib.axis.XTick at 0x7f8a5029cd10>,\n",
       "  <matplotlib.axis.XTick at 0x7f8a502ab4d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f8a502b9c10>,\n",
       "  <matplotlib.axis.XTick at 0x7f8a502bbed0>,\n",
       "  <matplotlib.axis.XTick at 0x7f8a502ba390>,\n",
       "  <matplotlib.axis.XTick at 0x7f8a502bf3d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f8a502c5890>,\n",
       "  <matplotlib.axis.XTick at 0x7f8a502c7bd0>,\n",
       "  <matplotlib.axis.XTick at 0x7f8a502d2090>],\n",
       " [Text(0, 0, 'scipy_cobyqa'),\n",
       "  Text(1, 0, 'scipy_tnc'),\n",
       "  Text(2, 0, 'adam_lr0.01_cond'),\n",
       "  Text(3, 0, 'adabelief_lr0.01_nocond'),\n",
       "  Text(4, 0, 'active_set_sgd_lr0.001_zo'),\n",
       "  Text(5, 0, 'active_set_sgd_lr0.01_zo'),\n",
       "  Text(6, 0, 'active_set_sgd_lr1.0_zo'),\n",
       "  Text(7, 0, 'active_set_v2_sgd_lr0.001_zo'),\n",
       "  Text(8, 0, 'active_set_v2_sgd_lr0.01_zo'),\n",
       "  Text(9, 0, 'active_set_v2_sgd_lr1.0_zo')])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_args = jnp.argsort(df[\"final_loss\"].values)\n",
    "sorted_f = df[\"final_loss\"].values[sorted_args][:10]\n",
    "sorted_names = df[\"name\"].values[sorted_args][:10]\n",
    "plt.plot(sorted_f)\n",
    "plt.xticks(ticks=range(len(sorted_names)), labels=sorted_names, rotation=45, ha=\"right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-16T10:05:31.591232Z",
     "iopub.status.busy": "2026-01-16T10:05:31.591106Z",
     "iopub.status.idle": "2026-01-16T10:05:31.816090Z",
     "shell.execute_reply": "2026-01-16T10:05:31.815627Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization: Final loss comparison (log scale)\n",
    "fig, ax = plt.subplots(figsize=(16, 6))\n",
    "\n",
    "df_valid = df[df[\"steps\"] > 0].copy()\n",
    "df_valid = df_valid.sort_values([\"category\", \"final_loss\"])\n",
    "\n",
    "# Create grouped bar chart\n",
    "x = range(len(df_valid))\n",
    "bars = ax.bar(x, df_valid[\"final_loss\"].values)\n",
    "\n",
    "# Color by category\n",
    "category_colors = {cat: colors[i] for i, cat in enumerate(categories)}\n",
    "for bar, cat in zip(bars, df_valid[\"category\"].values):\n",
    "    bar.set_color(category_colors[cat])\n",
    "\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(df_valid[\"name\"].values, rotation=45, ha=\"right\", fontsize=8)\n",
    "ax.set_ylabel(\"Final Loss (log scale)\")\n",
    "ax.set_title(\"Final Loss by Solver Configuration\")\n",
    "ax.axhline(y=1e-10, color=\"black\", linestyle=\"--\", label=\"Convergence threshold\")\n",
    "ax.set_ylim(3.0912078e6, 3.09120785e6)\n",
    "# Add legend\n",
    "from matplotlib.patches import Patch\n",
    "\n",
    "legend_elements = [Patch(facecolor=category_colors[cat], label=cat) for cat in categories]\n",
    "ax.legend(handles=legend_elements, loc=\"upper right\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-16T10:05:31.817741Z",
     "iopub.status.busy": "2026-01-16T10:05:31.817623Z",
     "iopub.status.idle": "2026-01-16T10:05:31.822524Z",
     "shell.execute_reply": "2026-01-16T10:05:31.822032Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      " Best Performer per Category\n",
      "============================================================\n",
      "\n",
      "Adam/SGD:\n",
      "  Best by loss: adam_lr0.01_cond\n",
      "    - Steps: 488, Loss: 3.09e+06, Dist: 1.30e+01\n",
      "\n",
      "Active Set v1:\n",
      "  Best by loss: active_set_sgd_lr0.001_zo\n",
      "    - Steps: 162, Loss: 3.09e+06, Dist: 1.30e+01\n",
      "\n",
      "Active Set v2:\n",
      "  Best by loss: active_set_v2_sgd_lr0.001_zo\n",
      "    - Steps: 162, Loss: 3.09e+06, Dist: 1.30e+01\n",
      "\n",
      "LBFGS:\n",
      "  Best by loss: lbfgs_zo_cond\n",
      "    - Steps: 2000, Loss: 3.09e+06, Dist: 1.29e+01\n",
      "\n",
      "Scipy:\n",
      "  Best by loss: scipy_cobyqa\n",
      "    - Steps: 246, Loss: 3.09e+06, Dist: 1.41e+01\n"
     ]
    }
   ],
   "source": [
    "# Best performers per category\n",
    "print(\"\\n\" + \"=\" * 60)\n",
    "print(\" Best Performer per Category\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "for category in categories:\n",
    "    cat_df = df[(df[\"category\"] == category) & (df[\"steps\"] > 0)]\n",
    "    if len(cat_df) > 0:\n",
    "        # Best by final loss\n",
    "        best = cat_df.loc[cat_df[\"final_loss\"].idxmin()]\n",
    "        print(f\"\\n{category}:\")\n",
    "        print(f\"  Best by loss: {best['name']}\")\n",
    "        print(\n",
    "            f\"    - Steps: {best['steps']}, Loss: {best['final_loss']:.2e}, Dist: {best['distance_to_opt']:.2e}\"\n",
    "        )\n",
    "\n",
    "        # Best by steps (among converged)\n",
    "        converged = cat_df[cat_df[\"converged\"]]\n",
    "        if len(converged) > 0:\n",
    "            fastest = converged.loc[converged[\"steps\"].idxmin()]\n",
    "            print(f\"  Fastest converged: {fastest['name']}\")\n",
    "            print(f\"    - Steps: {fastest['steps']}, Loss: {fastest['final_loss']:.2e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-16T10:05:31.823891Z",
     "iopub.status.busy": "2026-01-16T10:05:31.823784Z",
     "iopub.status.idle": "2026-01-16T10:05:31.829665Z",
     "shell.execute_reply": "2026-01-16T10:05:31.829255Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      " Active Set v1 vs v2 Comparison\n",
      "============================================================\n",
      "                        v1_name  v1_steps  v1_loss                            v2_name  v2_steps  v2_loss\n",
      "          active_set_lr0.001_ba        11 3.10e+06           active_set_v2_lr0.001_ba         7 3.13e+06\n",
      "          active_set_lr0.001_zo       306 3.10e+06           active_set_v2_lr0.001_zo       271 3.09e+06\n",
      "           active_set_lr0.01_ba        11 3.10e+06            active_set_v2_lr0.01_ba         7 3.13e+06\n",
      "           active_set_lr0.01_zo       306 3.10e+06            active_set_v2_lr0.01_zo       271 3.09e+06\n",
      "            active_set_lr1.0_ba        11 3.10e+06             active_set_v2_lr1.0_ba         7 3.13e+06\n",
      "            active_set_lr1.0_zo       306 3.10e+06             active_set_v2_lr1.0_zo       271 3.09e+06\n",
      "      active_set_sgd_lr0.001_ba       207 3.10e+06       active_set_v2_sgd_lr0.001_ba       207 3.10e+06\n",
      "      active_set_sgd_lr0.001_zo       162 3.09e+06       active_set_v2_sgd_lr0.001_zo       162 3.09e+06\n",
      "       active_set_sgd_lr0.01_ba       207 3.10e+06        active_set_v2_sgd_lr0.01_ba       207 3.10e+06\n",
      "       active_set_sgd_lr0.01_zo       162 3.09e+06        active_set_v2_sgd_lr0.01_zo       162 3.09e+06\n",
      "        active_set_sgd_lr1.0_ba       207 3.10e+06         active_set_v2_sgd_lr1.0_ba       207 3.10e+06\n",
      "        active_set_sgd_lr1.0_zo       162 3.09e+06         active_set_v2_sgd_lr1.0_zo       162 3.09e+06\n",
      "active_set_adabelief_lr0.001_ba        11 3.10e+06 active_set_v2_adabelief_lr0.001_ba         7 3.12e+06\n",
      "active_set_adabelief_lr0.001_zo       324 3.09e+06 active_set_v2_adabelief_lr0.001_zo       334 3.09e+06\n",
      " active_set_adabelief_lr0.01_ba        11 3.10e+06  active_set_v2_adabelief_lr0.01_ba         7 3.12e+06\n",
      " active_set_adabelief_lr0.01_zo       324 3.09e+06  active_set_v2_adabelief_lr0.01_zo       334 3.09e+06\n",
      "  active_set_adabelief_lr1.0_ba        11 3.10e+06   active_set_v2_adabelief_lr1.0_ba         7 3.12e+06\n",
      "  active_set_adabelief_lr1.0_zo       324 3.09e+06   active_set_v2_adabelief_lr1.0_zo       334 3.09e+06\n",
      "     active_set_adaw_lr0.001_ba        11 3.10e+06      active_set_v2_adaw_lr0.001_ba         7 3.13e+06\n",
      "     active_set_adaw_lr0.001_zo        20 3.10e+06      active_set_v2_adaw_lr0.001_zo       354 3.09e+06\n",
      "      active_set_adaw_lr0.01_ba        11 3.10e+06       active_set_v2_adaw_lr0.01_ba         7 3.13e+06\n",
      "      active_set_adaw_lr0.01_zo        20 3.10e+06       active_set_v2_adaw_lr0.01_zo       354 3.09e+06\n",
      "       active_set_adaw_lr1.0_ba        11 3.10e+06        active_set_v2_adaw_lr1.0_ba         7 3.13e+06\n",
      "       active_set_adaw_lr1.0_zo        20 3.10e+06        active_set_v2_adaw_lr1.0_zo       354 3.09e+06\n"
     ]
    }
   ],
   "source": [
    "# Active Set v1 vs v2 comparison\n",
    "# print(\"\\n\" + \"=\"*60)\n",
    "# print(\" Active Set v1 vs v2 Comparison\")\n",
    "# print(\"=\"*60)\n",
    "#\n",
    "# v1_df = df[df[\"category\"] == \"Active Set v1\"][[\"name\", \"steps\", \"final_loss\", \"converged\"]].reset_index(drop=True)\n",
    "# v2_df = df[df[\"category\"] == \"Active Set v2\"][[\"name\", \"steps\", \"final_loss\", \"converged\"]].reset_index(drop=True)\n",
    "#\n",
    "## Create side-by-side comparison\n",
    "# comparison = pd.DataFrame({\n",
    "#    \"v1_name\": v1_df[\"name\"],\n",
    "#    \"v1_steps\": v1_df[\"steps\"],\n",
    "#    \"v1_loss\": v1_df[\"final_loss\"].apply(lambda x: f\"{x:.2e}\"),\n",
    "#    \"v2_name\": v2_df[\"name\"],\n",
    "#    \"v2_steps\": v2_df[\"steps\"],\n",
    "#    \"v2_loss\": v2_df[\"final_loss\"].apply(lambda x: f\"{x:.2e}\"),\n",
    "# })\n",
    "#\n",
    "# print(comparison.to_string(index=False))"
   ]
  }
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