rosenbrook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import linalg as LA\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "ALLOWED_NAMES = {\n",
    "    k: v for k, v in np.__dict__.items() if not k.startswith(\"__\")\n",
    "}\n",
    "\n",
    "def f(function, x, x1, x2, d1, d2):\n",
    "  return eval(function, {\"__builtins__\": {}, \"x\": x, \"x1\": x1, \"x2\": x2, \"d1\": d1, \"d2\": d2}, ALLOWED_NAMES)\n",
    "\n",
    "def goldenSectionSearch(function, a, b, x1, x2, d1, d2):\n",
    "  goldenRatio = (5**(1/2) - 1) / (2)\n",
    "  e = 1e-6\n",
    "  time = 0\n",
    "  timeout = 100\n",
    "  xa, xb = 0, 0\n",
    "\n",
    "  while time < timeout:\n",
    "    d = goldenRatio * (b - a)\n",
    "    xa = a + d\n",
    "    xb = b - d\n",
    "    fxa = f(function, xa, x1, x2, d1, d2)\n",
    "    fxb = f(function, xb, x1, x2, d1, d2)\n",
    "\n",
    "    if math.fabs(xb - xa) < e:\n",
    "      break\n",
    "\n",
    "    if fxa > fxb:\n",
    "      b = xa\n",
    "    elif fxb > fxa:\n",
    "      a = xb\n",
    "\n",
    "    time += 1\n",
    "\n",
    "  return [xa, fxa]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def plot3D(function, min, range=5):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(projection='3d')\n",
    "\n",
    "    ax.set_title(f'f(x) = {function}', fontsize=10)\n",
    "    ax.set_xlabel(\"x1\")\n",
    "    ax.set_ylabel(\"x2\")\n",
    "    x1 = np.arange(-range, range, 0.15)\n",
    "    x2 = np.arange(-range, range, 0.15)\n",
    "    x1, x2 = np.meshgrid(x1, x2)\n",
    "    z = eval(function, {\"__builtins__\": {}, \"x1\": x1, \"x2\": x2}, ALLOWED_NAMES)\n",
    "\n",
    "    ax.plot_surface(x1, x2, z, rstride=1, cstride=1, cmap='viridis', label=f'f(x) = {function}')\n",
    "    \n",
    "    ax.scatter(min[0], min[1], color='red', label=f'Mínimo')\n",
    "\n",
    "def plotContour(function, min, range=5):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot()\n",
    "\n",
    "    ax.set_title(f'f(x) = {function}', fontsize=10)\n",
    "    ax.set_xlabel(\"x1\")\n",
    "    ax.set_ylabel(\"x2\")\n",
    "    x1 = np.arange(-range, range, 0.15)\n",
    "    x2 = np.arange(-range, range, 0.15)\n",
    "    x1, x2 = np.meshgrid(x1, x2)\n",
    "    z = eval(function, {\"__builtins__\": {}, \"x1\": x1, \"x2\": x2}, ALLOWED_NAMES)\n",
    "\n",
    "    ax.contour(x1, x2, z, colors='blue')\n",
    "    \n",
    "    ax.scatter(min[0], min[1], color='red', label=f'Mínimo')\n",
    "\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lmbda0: 3.128173868824198 => y0: [3.128173868824198, 3.0]\n",
      "lmbda1: -1.4359118495805663 => y1: [3.128173868824198, 1.5640881504194337]\n",
      "lmbda^1: -0.09723372252538973 => y3: [2.8240098788517707, 1.7037072047724697] d:[3.128173868824198,-1.4359118495805663]\n",
      "a1 = [ 3.12817387 -1.43591185] d1 = [ 0.90882638 -0.41717456]\n",
      "a2 = [ 0.         -1.43591185] d2 = [-0.41717456 -0.90882638]\n",
      "b2 = [-0.54441054 -1.18601348] d2 = [-0.41717456 -0.90882638]\n",
      "[[ 0.90882638 -0.41717456]\n",
      " [-0.41717456 -0.90882638]]\n",
      "lmbda0: 1.1134737526692074e-06 => y0: [2.824010890806088, 1.7037067402595443]\n",
      "lmbda1: 0.5131104006899724 => y1: [2.6099542839539764, 1.2373784735212041]\n",
      "lmbda^1: -1.857303610528694e-06 => y3: [2.6099546815202057, 1.2373793396352404] d:[-0.21405559489779424,-0.4663287312512656]\n",
      "a1 = [-0.21405559 -0.46632873] d1 = [-0.41717259 -0.90882728]\n",
      "a2 = [-0.21405661 -0.46632827] d2 = [-0.90882728  0.41717259]\n",
      "b2 = [-1.01195533e-06  4.64510729e-07] d2 = [-0.90882728  0.41717259]\n",
      "[[-0.41717259 -0.90882728]\n",
      " [-0.90882728  0.41717259]]\n",
      "lmbda0: -3.379330139793708e-08 => y0: [2.6099546956178448, 1.2373793703475147]\n",
      "lmbda1: 0.14351612917117879 => y1: [2.479523321901974, 1.2972503656864034]\n",
      "lmbda^1: -7.919320208914453e-06 => y3: [2.479524354829676, 1.2972498915485768] d:[-0.13043135961823182,0.05987102605116301]\n",
      "a1 = [-0.13043136  0.05987103] d1 = [-0.90882718  0.4171728 ]\n",
      "a2 = [-0.13043137  0.059871  ] d2 = [-0.41717281 -0.90882718]\n",
      "b2 = [-1.40976464e-08 -3.07122709e-08] d2 = [-0.41717281 -0.90882718]\n",
      "[[-0.90882718  0.4171728 ]\n",
      " [-0.41717281 -0.90882718]]\n",
      "lmbda0: -5.900139571360347e-07 => y0: [2.4795248910503993, 1.2972496454107998]\n",
      "lmbda1: 0.11617075217585328 => y1: [2.431061612437248, 1.1916705078543317]\n",
      "lmbda^1: -1.6180250767716733e-05 => y3: [2.4310623965765727, 1.1916722161552358] d:[-0.04846274239242776,-0.10557938369424513]\n",
      "a1 = [-0.04846274 -0.10557938] d1 = [-0.41716819 -0.9088293 ]\n",
      "a2 = [-0.04846328 -0.10557914] d2 = [-0.9088293   0.41716819]\n",
      "b2 = [-5.36221973e-07  2.46135054e-07] d2 = [-0.9088293   0.41716819]\n",
      "[[-0.41716819 -0.9088293 ]\n",
      " [-0.9088293   0.41716819]]\n",
      "lmbda0: -3.34501728759187e-07 => y0: [2.4310625361200535, 1.1916725201602087]\n",
      "lmbda1: 0.059439098031827745 => y1: [2.3770425421027572, 1.216468621087744]\n",
      "lmbda^1: -2.8165973917439153e-05 => y3: [2.3770440636245693, 1.2164679226728494] d:[-0.05401985447381552,0.02479640493250823]\n",
      "a1 = [-0.05401985  0.0247964 ] d1 = [-0.90882696  0.4171733 ]\n",
      "a2 = [-0.05401999  0.0247961 ] d2 = [-0.4171733  -0.90882696]\n",
      "b2 = [-1.39545191e-07 -3.04004188e-07] d2 = [-0.4171733  -0.90882696]\n",
      "[[-0.90882696  0.4171733 ]\n",
      " [-0.4171733  -0.90882696]]\n",
      "lmbda0: 2.4940329940731136e-07 => y0: [2.3770438369601283, 1.2164680267172479]\n",
      "lmbda1: 0.0585608724164799 => y1: [2.3526138043089944, 1.1632463273565194]\n",
      "lmbda^1: -1.2314336240876079e-05 => y3: [2.352614105151422, 1.1632469827451395] d:[-0.024430259315574965,-0.05322159531632997]\n",
      "a1 = [-0.02443026 -0.0532216 ] d1 = [-0.41717717 -0.90882518]\n",
      "a2 = [-0.02443003 -0.0532217 ] d2 = [ 0.90882518 -0.41717717]\n",
      "b2 = [ 2.26663998e-07 -1.04045364e-07] d2 = [ 0.90882518 -0.41717717]\n",
      "[[-0.41717717 -0.90882518]\n",
      " [ 0.90882518 -0.41717717]]\n",
      "lmbda0: -3.297686139076616e-07 => y0: [2.352614242723361, 1.163247282447159]\n",
      "lmbda1: -0.034770236489820756 => y1: [2.3210141763451895, 1.1777526314761622]\n",
      "lmbda^1: 3.3320013677628155e-05 => y3: [2.3210131234351294, 1.1777531148045763] d:[-0.03159992880623275,0.014505648731022669]\n",
      "a1 = [-0.03159993  0.01450565] d1 = [-0.90882122  0.41718579]\n",
      "a2 = [-0.03160007  0.01450535] d2 = [-0.41718579 -0.90882122]\n",
      "b2 = [-1.37574781e-07 -2.99700715e-07] d2 = [-0.41718579 -0.90882122]\n",
      "[[-0.90882122  0.41718579]\n",
      " [-0.41718579 -0.90882122]]\n",
      "lmbda0: 6.293427486756999e-08 => y0: [2.321013066239125, 1.1777531410598618]\n",
      "lmbda1: 0.03678050639908125 => y1: [2.3056687614590374, 1.144326236300542]\n",
      "lmbda^1: -1.4192256793371863e-05 => y3: [2.305668979230163, 1.1443267107033854] d:[-0.015344361976092014,-0.03342687850403436]\n",
      "a1 = [-0.01534436 -0.03342688] d1 = [-0.41718735 -0.90882051]\n",
      "a2 = [-0.0153443 -0.0334269] d2 = [ 0.90882051 -0.41718735]\n",
      "b2 = [ 5.71959596e-08 -2.62553833e-08] d2 = [ 0.90882051 -0.41718735]\n",
      "[[-0.41718735 -0.90882051]\n",
      " [ 0.90882051 -0.41718735]]\n",
      "lmbda0: 1.7213348506160025e-06 => y0: [2.3056682611110393, 1.1443251463189723]\n",
      "lmbda1: -0.023508317339694898 => y1: [2.28430342020755, 1.1541325189188278]\n",
      "lmbda^1: 3.7777963547467415e-05 => y3: [2.28430261306024, 1.154132889362293] d:[-0.021365559022612945,0.009805808215442413]\n",
      "a1 = [-0.02136556  0.00980581] d1 = [-0.90885105  0.4171208 ]\n",
      "a2 = [-0.02136484  0.00980737] d2 = [0.4171208  0.90885105]\n",
      "b2 = [7.18004572e-07 1.56443699e-06] d2 = [0.4171208  0.90885105]\n",
      "[[-0.90885105  0.4171208 ]\n",
      " [ 0.4171208   0.90885105]]\n",
      "lmbda0: -1.2613094810490585e-07 => y0: [2.284302727694485, 1.1541328367504509]\n",
      "lmbda1: -0.02581270870897996 => y1: [2.2735357099286073, 1.130672929262911]\n",
      "lmbda^1: 1.4936673073032783e-05 => y3: [2.2735355491068954, 1.1306725788491567] d:[-0.010766903131632866,-0.023459960099382116]\n",
      "a1 = [-0.0107669  -0.02345996] d1 = [-0.41711636 -0.90885309]\n",
      "a2 = [-0.01076702 -0.02345991] d2 = [-0.90885309  0.41711636]\n",
      "b2 = [-1.14634502e-07  5.26112821e-08] d2 = [-0.90885309  0.41711636]\n",
      "[[-0.41711636 -0.90885309]\n",
      " [-0.90885309  0.41711636]]\n",
      "lmbda0: 2.8355967534847197e-07 => y0: [2.2735354308295155, 1.1306723211350693]\n",
      "lmbda1: 0.017251781484663782 => y1: [2.2578560959004994, 1.137868321453185]\n",
      "lmbda^1: -4.199029639013309e-05 => y3: [2.257856754285387, 1.1378680193018205] d:[-0.015679453206395966,0.007195742604028332]\n",
      "a1 = [-0.01567945  0.00719574] d1 = [-0.90885995  0.41710142]\n",
      "a2 = [-0.01567933  0.007196  ] d2 = [0.41710142 0.90885995]\n",
      "b2 = [1.18273144e-07 2.57716031e-07] d2 = [0.41710142 0.90885995]\n",
      "[[-0.90885995  0.41710142]\n",
      " [ 0.41710142  0.90885995]]\n",
      "lmbda0: 9.056970028701002e-07 => y0: [2.257855931133657, 1.137868397069329]\n",
      "lmbda1: -0.019390490458939744 => y1: [2.2497681299743895, 1.1202451569402727]\n",
      "lmbda^1: 0.00014277854674436924 => y3: [2.2497669750923652, 1.1202426407735953] d:[-0.008088624310997439,-0.017622862361547753]\n",
      "a1 = [-0.00808862 -0.01762286] d1 = [-0.41714387 -0.90884046]\n",
      "a2 = [-0.0080878  -0.01762324] d2 = [ 0.90884046 -0.41714387]\n",
      "b2 = [ 8.23134083e-07 -3.77805956e-07] d2 = [ 0.90884046 -0.41714387]\n",
      "[[-0.41714387 -0.90884046]\n",
      " [ 0.90884046 -0.41714387]]\n",
      "lmbda0: 5.038877405313166e-07 => y0: [2.2497667648986814, 1.1202421828200275]\n",
      "lmbda1: -0.013352271713334219 => y1: [2.2376316800817326, 1.125812001164656]\n",
      "lmbda^1: 4.1914583788318806e-05 => y3: [2.237631171435893, 1.1258122346020787] d:[-0.012135295010632596,0.005569360391060707]\n",
      "a1 = [-0.0121353   0.00556936] d1 = [-0.90885621  0.41710958]\n",
      "a2 = [-0.01213508  0.00556982] d2 = [0.41710958 0.90885621]\n",
      "b2 = [2.10176401e-07 4.57961499e-07] d2 = [0.41710958 0.90885621]\n",
      "[[-0.90885621  0.41710958]\n",
      " [ 0.41710958  0.90885621]]\n",
      "lmbda0: -1.4997183256672024e-07 => y0: [2.2376313077387238, 1.1258121720473913]\n",
      "lmbda1: -0.015251536465175044 => y1: [2.231269745837171, 1.111950718490088]\n",
      "lmbda^1: -4.585827149559954e-05 => y3: [2.231270037561153, 1.1119513541552573] d:[-0.0063614255987221036,-0.013861516111990602]\n",
      "a1 = [-0.00636143 -0.01386152] d1 = [-0.41710064 -0.90886031]\n",
      "a2 = [-0.00636156 -0.01386145] d2 = [-0.90886031  0.41710064]\n",
      "b2 = [-1.36303446e-07  6.25533471e-08] d2 = [-0.90886031  0.41710064]\n",
      "[[-0.41710064 -0.90886031]\n",
      " [-0.90886031  0.41710064]]\n",
      "lmbda0: 2.280391572728321e-07 => y0: [2.231269942445875, 1.111951146899519]\n",
      "lmbda1: 0.010731225215758176 => y1: [2.2215167578036863, 1.1164271477894545]\n",
      "lmbda^1: -9.460795009761611e-05 => y3: [2.2215176805414907, 1.1164267243437938] d:[-0.00975327975746687,0.004475793634197256]\n",
      "a1 = [-0.00975328  0.00447579] d1 = [-0.90886917  0.41708133]\n",
      "a2 = [-0.00975318  0.004476  ] d2 = [0.41708133 0.90886917]\n",
      "b2 = [9.51108739e-08 2.07257760e-07] d2 = [0.41708133 0.90886917]\n",
      "[[-0.90886917  0.41708133]\n",
      " [ 0.41708133  0.90886917]]\n",
      "lmbda0: 1.2141076783158963e-06 => y0: [2.2215165770764527, 1.116427230725433]\n",
      "lmbda1: -0.012399504328451261 => y1: [2.21634497538015, 1.1051577035176892]\n",
      "lmbda^1: 0.0001709054143333734 => y3: [2.216344091336831, 1.1051557775810157] d:[-0.005172705161340829,-0.011269020826104592]\n",
      "a1 = [-0.00517271 -0.01126902] d1 = [-0.41717032 -0.90882833]\n",
      "a2 = [-0.0051716  -0.01126953] d2 = [ 0.90882833 -0.41717032]\n",
      "b2 = [ 1.10341544e-06 -5.06489681e-07] d2 = [ 0.90882833 -0.41717032]\n",
      "[[-0.41717032 -0.90882833]\n",
      " [ 0.90882833 -0.41717032]]\n",
      "lmbda0: 1.0706534859287927e-06 => y0: [2.216343644691978, 1.1051548045407995]\n",
      "lmbda1: -0.008865927270307637 => y1: [2.2082860388452947, 1.1088534062200663]\n",
      "lmbda^1: 0.00017341199095594596 => y3: [2.208284641482369, 1.1088540474332105] d:[-0.008058052491536127,0.003697628639050654]\n",
      "a1 = [-0.00805805  0.00369763] d1 = [-0.9088787   0.41706056]\n",
      "a2 = [-0.00805761  0.0036986 ] d2 = [0.41706056 0.9088787 ]\n",
      "b2 = [4.46527342e-07 9.73094139e-07] d2 = [0.41706056 0.9088787 ]\n",
      "[[-0.9088787   0.41706056]\n",
      " [ 0.41706056  0.9088787 ]]\n",
      "lmbda0: 3.171289328670109e-07 => y0: [2.2082843532506375, 1.1088541796951816]\n",
      "lmbda1: -0.010335891804345085 => y1: [2.203973660403544, 1.0994601078107886]\n",
      "lmbda^1: 5.248172889049378e-05 => y3: [2.2039734341558037, 1.0994596148005962] d:[-0.004310981078825105,-0.009393939622421854]\n",
      "a1 = [-0.00431098 -0.00939394] d1 = [-0.41708845 -0.9088659 ]\n",
      "a2 = [-0.00431069 -0.00939407] d2 = [ 0.9088659  -0.41708845]\n",
      "b2 = [ 2.88227673e-07 -1.32270815e-07] d2 = [ 0.9088659  -0.41708845]\n",
      "[[-0.41708845 -0.9088659 ]\n",
      " [ 0.9088659  -0.41708845]]\n",
      "lmbda0: -1.0835322637240358e-07 => y0: [2.203973479348683, 1.0994597132791488]\n",
      "lmbda1: -0.007484203327841288 => y1: [2.1971713421474077, 1.1025812880335795]\n",
      "lmbda^1: 0.00017426952431773605 => y3: [2.197170156750069, 1.102581832046089] d:[-0.006802092008395988,0.003121673232983291]\n",
      "a1 = [-0.00680209  0.00312167] d1 = [-0.90885986  0.41710161]\n",
      "a2 = [-0.00680214  0.00312157] d2 = [-0.41710161 -0.90885986]\n",
      "b2 = [-4.51943048e-08 -9.84778984e-08] d2 = [-0.41710161 -0.90885986]\n",
      "[[-0.90885986  0.41710161]\n",
      " [-0.41710161 -0.90885986]]\n",
      "lmbda0: 2.0788979683900264e-07 => y0: [2.197169967807377, 1.1025819187572572]\n",
      "lmbda1: 0.00878825181271303 => y1: [2.193504373856499, 1.0945946294228872]\n",
      "lmbda^1: 5.497810610780129e-05 => y3: [2.1935041723186983, 1.0945941903016139] d:[-0.003665782893570224,-0.007987202623201739]\n",
      "a1 = [-0.00366578 -0.0079872 ] d1 = [-0.41712311 -0.90885   ]\n",
      "a2 = [-0.00366559 -0.00798729] d2 = [ 0.90885    -0.41712311]\n",
      "b2 = [ 1.88940641e-07 -8.67156377e-08] d2 = [ 0.90885    -0.41712311]\n",
      "[[-0.41712311 -0.90885   ]\n",
      " [ 0.90885    -0.41712311]]\n",
      "lmbda0: -7.237837622719377e-07 => y0: [2.1935044742256293, 1.094594848112483]\n",
      "lmbda1: -0.006429638371805567 => y1: [2.18766089741996, 1.0972767988406729]\n",
      "lmbda^1: -0.00017117105168575433 => y3: [2.1876618976194697, 1.097276339655748] d:[-0.005843274898738482,0.002682608539058995]\n",
      "a1 = [-0.00584327  0.00268261] d1 = [-0.90880303  0.41722541]\n",
      "a2 = [-0.00584358  0.00268195] d2 = [-0.41722541 -0.90880303]\n",
      "b2 = [-3.01980977e-07 -6.57776875e-07] d2 = [-0.41722541 -0.90880303]\n",
      "[[-0.90880303  0.41722541]\n",
      " [-0.41722541 -0.90880303]]\n",
      "lmbda0: 1.1829697313637663e-06 => y0: [2.1876608225329885, 1.097276833220782]\n",
      "lmbda1: 0.0075944068591227145 => y1: [2.184492242998321, 1.0903750132235368]\n",
      "lmbda^1: 0.0001527874991117341 => y3: [2.1844917587147186, 1.0903739587871306] d:[-0.0031696546211485987,-0.006901326432211086]\n",
      "a1 = [-0.00316965 -0.00690133] d1 = [-0.41736697 -0.90873803]\n",
      "a2 = [-0.00316858 -0.00690182] d2 = [ 0.90873803 -0.41736697]\n",
      "b2 = [ 1.07500957e-06 -4.93732487e-07] d2 = [ 0.90873803 -0.41736697]\n",
      "[[-0.41736697 -0.90873803]\n",
      " [ 0.90873803 -0.41736697]]\n",
      "lmbda0: -7.032999848327241e-07 => y0: [2.1844920522489026, 1.0903745979025752]\n",
      "lmbda1: -0.005599766113023863 => y1: [2.1794033318079173, 1.0927117553200523]\n",
      "lmbda^1: -0.00017878616434561593 => y3: [2.1794042415482466, 1.092711337354377] d:[-0.0050884269068012244,0.0023377965329216543]\n",
      "a1 = [-0.00508843  0.0023378 ] d1 = [-0.90868561  0.4174811 ]\n",
      "a2 = [-0.00508872  0.00233716] d2 = [-0.4174811  -0.90868561]\n",
      "b2 = [-2.93614449e-07 -6.39078568e-07] d2 = [-0.4174811  -0.90868561]\n",
      "[[-0.90868561  0.4174811 ]\n",
      " [-0.4174811  -0.90868561]]\n",
      "lmbda0: 7.951801742179368e-07 => y0: [2.1794035189794676, 1.0927116693270706]\n",
      "lmbda1: 0.006648758797328329 => y1: [2.1766277878456486, 1.08667003790685]\n",
      "lmbda^1: 2.686646025883818e-05 => y3: [2.1766277132521656, 1.0866698755985185] d:[-0.0027764537025980474,-0.006041299447527182]\n",
      "a1 = [-0.00277645 -0.0060413 ] d1 = [-0.41758977 -0.90863567]\n",
      "a2 = [-0.00277573 -0.00604163] d2 = [ 0.90863567 -0.41758977]\n",
      "b2 = [ 7.22529065e-07 -3.32059107e-07] d2 = [ 0.90863567 -0.41758977]\n",
      "[[-0.41758977 -0.90863567]\n",
      " [ 0.90863567 -0.41758977]]\n",
      "lmbda0: 1.050053802112308e-06 => y0: [2.176627274760436, 1.0866689214821785]\n",
      "lmbda1: -0.004933298581314981 => y1: [2.1721447036988075, 1.088729016521213]\n",
      "lmbda^1: 0.00047465911042148293 => y3: [2.172142575797481, 1.0887299939112114] d:[-0.004483009553358119,0.0020591409226944357]\n",
      "a1 = [-0.00448301  0.00205914] d1 = [-0.90872453  0.41739636]\n",
      "a2 = [-0.00448257  0.0020601 ] d2 = [0.41739636 0.90872453]\n",
      "b2 = [4.38288626e-07 9.54209630e-07] d2 = [0.41739636 0.90872453]\n",
      "[[-0.90872453  0.41739636]\n",
      " [ 0.41739636  0.90872453]]\n",
      "lmbda0: 6.300230016670014e-07 => y0: [2.1721420032801224, 1.0887302568805197]\n",
      "lmbda1: -0.005878968668051409 => y1: [2.1696881431510784, 1.083387893820317]\n",
      "lmbda^1: 0.0002450209859141812 => y3: [2.1696875417635715, 1.0833865848936859] d:[-0.002454432646402438,-0.005342100090894419]\n",
      "a1 = [-0.00245443 -0.0053421 ] d1 = [-0.41749374 -0.9086798 ]\n",
      "a2 = [-0.00245386 -0.00534236] d2 = [ 0.9086798  -0.41749374]\n",
      "b2 = [ 5.72489170e-07 -2.63030659e-07] d2 = [ 0.9086798  -0.41749374]\n",
      "[[-0.41749374 -0.9086798 ]\n",
      " [ 0.9086798  -0.41749374]]\n",
      "lmbda0: 1.0829916454639743e-06 => y0: [2.1696870896213363, 1.0833856008010565]\n",
      "lmbda1: -0.004391887730811432 => y1: [2.165696269966393, 1.085219186447207]\n",
      "lmbda^1: 0.00010844529917116292 => y3: [2.1656958371317288, 1.0852193851842307] d:[-0.003991271797178619,0.0018326015535210516]\n",
      "a1 = [-0.00399127  0.0018326 ] d1 = [-0.90878272  0.41726966]\n",
      "a2 = [-0.00399082  0.00183359] d2 = [0.41726966 0.90878272]\n",
      "b2 = [4.51899541e-07 9.84204063e-07] d2 = [0.41726966 0.90878272]\n",
      "[[-0.90878272  0.41726966]\n",
      " [ 0.41726966  0.90878272]]\n",
      "lmbda0: -1.6665318475150588e-07 => y0: [2.165695988583263, 1.085219315644913]\n",
      "lmbda1: -0.005247988266923714 => y1: [2.1635061623062906, 1.0804500345956929]\n",
      "lmbda^1: -0.00016620862822416523 => y3: [2.1635065262491397, 1.0804508273029116] d:[-0.0021896748254381926,-0.00476935058853778]\n",
      "a1 = [-0.00218967 -0.00476935] d1 = [-0.4172408  -0.90879597]\n",
      "a2 = [-0.00218983 -0.00476928] d2 = [-0.90879597  0.4172408 ]\n",
      "b2 = [-1.51453743e-07  6.95345081e-08] d2 = [-0.90879597  0.4172408 ]\n",
      "[[-0.4172408  -0.90879597]\n",
      " [-0.90879597  0.4172408 ]]\n",
      "lmbda0: -5.683406271754178e-07 => y0: [2.1635067633840377, 1.080451343808583]\n",
      "lmbda1: 0.003940666729364023 => y1: [2.15992550134237, 1.0820955507483652]\n",
      "lmbda^1: -0.00024094491586645482 => y3: [2.159926364172115, 1.082095154460613] d:[-0.003581024906769592,0.001644723445453522]\n",
      "a1 = [-0.00358102  0.00164472] d1 = [-0.90873578  0.41737187]\n",
      "a2 = [-0.00358126  0.00164421] d2 = [-0.41737187 -0.90873578]\n",
      "b2 = [-2.37209386e-07 -5.16471460e-07] d2 = [-0.41737187 -0.90873578]\n",
      "[[-0.90873578  0.41737187]\n",
      " [-0.41737187 -0.90873578]]\n",
      "lmbda0: -1.0776367645335109e-07 => y0: [2.1599264621008243, 1.0820951094830862]\n",
      "lmbda1: 0.0047243061837096 => y1: [2.157954669610705, 1.077801963399694]\n",
      "lmbda^1: -0.00019971311423421238 => y3: [2.157955063383966, 1.077802820806251] d:[-0.001971694561410331,-0.004293191060918966]\n",
      "a1 = [-0.00197169 -0.00429319] d1 = [-0.41735114 -0.9087453 ]\n",
      "a2 = [-0.00197179 -0.00429315] d2 = [-0.9087453   0.41735114]\n",
      "b2 = [-9.79297349e-08  4.49752930e-08] d2 = [-0.9087453   0.41735114]\n",
      "[[-0.41735114 -0.9087453 ]\n",
      " [-0.9087453   0.41735114]]\n",
      "lmbda0: 5.658238527170327e-07 => y0: [2.157954827236737, 1.0778023066164817]\n",
      "lmbda1: 0.0035634004601017713 => y1: [2.1547166038018264, 1.0792894958528096]\n",
      "lmbda^1: -0.0001304691117517326 => y3: [2.1547170263207716, 1.0792893018876368] d:[-0.00323845958213953,0.0014866750465587497]\n",
      "a1 = [-0.00323846  0.00148668] d1 = [-0.90881156  0.41720684]\n",
      "a2 = [-0.00323822  0.00148719] d2 = [0.41720684 0.90881156]\n",
      "b2 = [2.36065576e-07 5.14227253e-07] d2 = [0.41720684 0.90881156]\n",
      "[[-0.90881156  0.41720684]\n",
      " [ 0.41720684  0.90881156]]\n",
      "lmbda0: 1.3249372468919139e-06 => y0: [2.1547158222024816, 1.079289854660512]\n",
      "lmbda1: -0.004280244064875597 => y1: [2.152930075123122, 1.0753999193624049]\n",
      "lmbda^1: 0.0005143148096671273 => y3: [2.152929156067657, 1.0753979189953717] d:[-0.0017869511976496,-0.003889382525231966]\n",
      "a1 = [-0.00178695 -0.00388938] d1 = [-0.41748814 -0.90868237]\n",
      "a2 = [-0.00178575 -0.00388994] d2 = [ 0.90868237 -0.41748814]\n",
      "b2 = [ 1.20394707e-06 -5.53145554e-07] d2 = [ 0.90868237 -0.41748814]\n",
      "[[-0.41748814 -0.90868237]\n",
      " [ 0.90868237 -0.41748814]]\n",
      "lmbda0: 1.7129800974114709e-06 => y0: [2.1529284409187905, 1.0753963624405498]\n",
      "lmbda1: -0.0032402933499746066 => y1: [2.1499840434645323, 1.0767491464681191]\n",
      "lmbda^1: 0.0007931246237077533 => y3: [2.149981707623207, 1.0767502181599] d:[-0.002945112603124578,0.0013512274727474072]\n",
      "a1 = [-0.00294511  0.00135123] d1 = [-0.90890295  0.4170077 ]\n",
      "a2 = [-0.0029444   0.00135278] d2 = [0.4170077  0.90890295]\n",
      "b2 = [7.14325794e-07 1.55693245e-06] d2 = [0.4170077  0.90890295]\n",
      "[[-0.90890295  0.4170077 ]\n",
      " [ 0.4170077   0.90890295]]\n",
      "lmbda0: -9.208697415283556e-08 => y0: [2.1499817913213297, 1.0767501797589225]\n",
      "lmbda1: -0.003898312894984989 => y1: [2.148356164819009, 1.0732069916603089]\n",
      "lmbda^1: 0.00031583041300693905 => y3: [2.1483556514231537, 1.07320587260162] d:[-0.0016255428041982434,-0.0035432264995911567]\n",
      "a1 = [-0.00162554 -0.00354323] d1 = [-0.41698623 -0.9089128 ]\n",
      "a2 = [-0.00162563 -0.00354319] d2 = [-0.9089128   0.41698623]\n",
      "b2 = [-8.36990297e-08  3.83990003e-08] d2 = [-0.9089128   0.41698623]\n",
      "[[-0.41698623 -0.9089128 ]\n",
      " [-0.9089128   0.41698623]]\n",
      "lmbda0: 1.4619253604098627e-06 => y0: [2.1483550418204067, 1.0732045438389435]\n",
      "lmbda1: 0.0029658731698110966 => y1: [2.145659321725614, 1.0744412721154601]\n",
      "lmbda^1: 0.0003860820387947681 => y3: [2.145658280721147, 1.0744417490810232] d:[-0.0026963296975397455,0.0012353995138401075]\n",
      "a1 = [-0.00269633  0.0012354 ] d1 = [-0.90911823  0.41653816]\n",
      "a2 = [-0.00269572  0.00123673] d2 = [0.41653816 0.90911823]\n",
      "b2 = [6.08947631e-07 1.32906284e-06] d2 = [0.41653816 0.90911823]\n",
      "[[-0.90911823  0.41653816]\n",
      " [ 0.41653816  0.90911823]]\n",
      "lmbda0: 5.503512727323689e-07 => y0: [2.145657780386771, 1.0744419783233317]\n",
      "lmbda1: -0.0035704480005928057 => y1: [2.144170552533211, 1.071196018952586]\n",
      "lmbda^1: 0.0006743462278605296 => y3: [2.1441695492893196, 1.0711938302067172] d:[-0.001487728187935744,-0.0032457301284372075]\n",
      "a1 = [-0.00148773 -0.00324573] d1 = [-0.41667829 -0.90905401]\n",
      "a2 = [-0.00148723 -0.00324596] d2 = [ 0.90905401 -0.41667829]\n",
      "b2 = [ 5.00299028e-07 -2.29319425e-07] d2 = [ 0.90905401 -0.41667829]\n",
      "[[-0.41667829 -0.90905401]\n",
      " [ 0.90905401 -0.41667829]]\n",
      "lmbda0: 2.806682491134275e-07 => y0: [2.1441694323409535, 1.0711935750641186]\n",
      "lmbda1: -0.0027250042348691875 => y1: [2.141692256300666, 1.0723290251711157]\n",
      "lmbda^1: 0.0005911429431810314 => y3: [2.1416907918663974, 1.072329696233608] d:[-0.0024772929886536232,0.0011351949643985115]\n",
      "a1 = [-0.00247729  0.00113519] d1 = [-0.90909693  0.41658466]\n",
      "a2 = [-0.00247718  0.00113545] d2 = [0.41658466 0.90909693]\n",
      "b2 = [1.16922086e-07 2.55154641e-07] d2 = [0.41658466 0.90909693]\n",
      "[[-0.90909693  0.41658466]\n",
      " [ 0.41658466  0.90909693]]\n",
      "lmbda0: 1.047553778419417e-06 => y0: [2.141689839538477, 1.072330132628441]\n",
      "lmbda1: -0.0032897371767717644 => y1: [2.1403193855004004, 1.069339442670964]\n",
      "lmbda^1: 0.0007764568260156707 => y3: [2.1403183206625664, 1.0693371208681737] d:[-0.0013714063659970854,-0.0029902535626440407]\n",
      "a1 = [-0.00137141 -0.00299025] d1 = [-0.41687412 -0.90896423]\n",
      "a2 = [-0.00137045 -0.00299069] d2 = [ 0.90896423 -0.41687412]\n",
      "b2 = [ 9.52188863e-07 -4.36698039e-07] d2 = [ 0.90896423 -0.41687412]\n",
      "[[-0.41687412 -0.90896423]\n",
      " [ 0.90896423 -0.41687412]]\n",
      "lmbda0: 3.585649250506157e-07 => y0: [2.140318171186128, 1.0693367949454837]\n",
      "lmbda1: -0.00251619844013499 => y1: [2.138031036815153, 1.0703857329604505]\n",
      "lmbda^1: 0.0007564358858730855 => y3: [2.1380293066315694, 1.0703865261682675] d:[-0.0022872838474135015,0.0010486120922768016]\n",
      "a1 = [-0.00228728  0.00104861] d1 = [-0.90902362  0.41674459]\n",
      "a2 = [-0.00228713  0.00104894] d2 = [0.41674459 0.90902362]\n",
      "b2 = [1.49429990e-07 3.25943984e-07] d2 = [0.41674459 0.90902362]\n",
      "[[-0.90902362  0.41674459]\n",
      " [ 0.41674459  0.90902362]]\n",
      "lmbda0: 5.090147154537287e-07 => y0: [2.1380288439251682, 1.0703867382973953]\n",
      "lmbda1: -0.003044222312670586 => y1: [2.136760180752459, 1.0676194682991529]\n",
      "lmbda^1: 0.00024201090356341823 => y3: [2.136759873610158, 1.0676187986409778] d:[-0.0012691258791104332,-0.0027670578691145753]\n",
      "a1 = [-0.00126913 -0.00276706] d1 = [-0.41689658 -0.90895393]\n",
      "a2 = [-0.00126866 -0.00276727] d2 = [ 0.90895393 -0.41689658]\n",
      "b2 = [ 4.62670919e-07 -2.12206489e-07] d2 = [ 0.90895393 -0.41689658]\n",
      "[[-0.41689658 -0.90895393]\n",
      " [ 0.90895393 -0.41689658]]\n",
      "lmbda0: 1.0345464982277887e-06 => y0: [2.136759442311264, 1.067617858285874]\n",
      "lmbda1: -0.0023343033319989572 => y1: [2.1346376681272523, 1.068591021354348]\n",
      "lmbda^1: 0.0007003395877962228 => y3: [2.134636181862739, 1.0685917022404023] d:[-0.002122205482905759,0.0009722227133701633]\n",
      "a1 = [-0.00212221  0.00097222] d1 = [-0.9091386   0.41649369]\n",
      "a2 = [-0.00212177  0.00097316] d2 = [0.41649369 0.9091386 ]\n",
      "b2 = [4.30882050e-07 9.40546068e-07] d2 = [0.41649369 0.9091386 ]\n",
      "[[-0.9091386   0.41649369]\n",
      " [ 0.41649369  0.9091386 ]]\n",
      "lmbda0: -7.845516622750603e-07 => y0: [2.1346368951289425, 1.0685913754795824]\n",
      "lmbda1: -0.0028236422050536662 => y1: [2.133460865956152, 1.0660242933447637]\n",
      "lmbda^1: -0.00025617176734775573 => y3: [2.133461167038905, 1.066024951042438] d:[-0.0011753159065870378,-0.0025674088956386676]\n",
      "a1 = [-0.00117532 -0.00256741] d1 = [-0.41624107 -0.90925429]\n",
      "a2 = [-0.00117603 -0.00256708] d2 = [-0.90925429  0.41624107]\n",
      "b2 = [-7.13356939e-07  3.26562613e-07] d2 = [-0.90925429  0.41624107]\n",
      "[[-0.41624107 -0.90925429]\n",
      " [-0.90925429  0.41624107]]\n",
      "lmbda0: 1.5169497705053883e-06 => y0: [2.133460535622105, 1.0660235717493471]\n",
      "lmbda1: 0.0021745563338903106 => y1: [2.1314833109403795, 1.066928711411078]\n",
      "lmbda^1: 0.000318738007718587 => y3: [2.131482680522467, 1.0669289994738573] d:[-0.001977856098525521,0.0009037603686399276]\n",
      "a1 = [-0.00197786  0.00090376] d1 = [-0.90954444  0.41560668]\n",
      "a2 = [-0.00197722  0.00090514] d2 = [0.41560668 0.90954444]\n",
      "b2 = [6.30454312e-07 1.37973289e-06] d2 = [0.41560668 0.90954444]\n",
      "[[-0.90954444  0.41560668]\n",
      " [ 0.41560668  0.90954444]]\n",
      "lmbda0: 8.0180819886066e-08 => y0: [2.131482607594448, 1.066929032797542]\n",
      "lmbda1: -0.002628152359337782 => y1: [2.130390329905354, 1.0645386114384927]\n",
      "lmbda^1: -9.88211354383903e-06 => y3: [2.1303903407000866, 1.0645386350605788] d:[-0.0010923506171129915,-0.002390388035364621]\n",
      "a1 = [-0.00109235 -0.00239039] d1 = [-0.41563443 -0.90953176]\n",
      "a2 = [-0.00109228 -0.00239042] d2 = [ 0.90953176 -0.41563443]\n",
      "b2 = [ 7.29270020e-08 -3.33259096e-08] d2 = [ 0.90953176 -0.41563443]\n",
      "[[-0.41563443 -0.90953176]\n",
      " [ 0.90953176 -0.41563443]]\n",
      "lmbda0: -3.427298914567615e-07 => y0: [2.130390483150431, 1.0645389467842994]\n",
      "lmbda1: -0.0020280273790717236 => y1: [2.1285459278441743, 1.0653818647947995]\n",
      "lmbda^1: 0.0003647144879311226 => y3: [2.128545255160084, 1.0653821723329002] d:[-0.001844412855912303,0.0008432297342206851]\n",
      "a1 = [-0.00184441  0.00084323] d1 = [-0.9094615   0.41578814]\n",
      "a2 = [-0.00184456  0.00084292] d2 = [-0.41578814 -0.9094615 ]\n",
      "b2 = [-1.42503020e-07 -3.11699638e-07] d2 = [-0.41578814 -0.9094615 ]\n",
      "[[-0.9094615   0.41578814]\n",
      " [-0.41578814 -0.9094615 ]]\n",
      "lmbda0: 1.6544478649384263e-06 => y0: [2.1285437505034412, 1.065382860232693]\n",
      "lmbda1: 0.0024594392071388958 => y1: [2.1275211448617424, 1.0631460949532823]\n",
      "lmbda^1: 0.0008279414602378511 => y3: [2.1275202969583664, 1.0631442436121115] d:[-0.0010241102983417072,-0.002236077379617951]\n",
      "a1 = [-0.00102411 -0.00223608] d1 = [-0.41639983 -0.9091816 ]\n",
      "a2 = [-0.00102261 -0.00223677] d2 = [ 0.9091816  -0.41639983]\n",
      "b2 = [ 1.50419322e-06 -6.88911653e-07] d2 = [ 0.9091816  -0.41639983]\n",
      "[[-0.41639983 -0.9091816 ]\n",
      " [ 0.9091816  -0.41639983]]\n",
      "lmbda0: 1.5766119281798547e-06 => y0: [2.1275196404574284, 1.0631428101855558]\n",
      "lmbda1: -0.0018999458033539553 => y1: [2.125792244691837, 1.0639339472942169]\n",
      "lmbda^1: 0.0012688919519766061 => y3: [2.1257900519802235, 1.0639349493428636] d:[-0.0017280522665292786,0.0007897036821054026]\n",
      "a1 = [-0.00172805  0.0007897 ] d1 = [-0.90952682  0.41564523]\n",
      "a2 = [-0.0017274   0.00079114] d2 = [0.41564523 0.90952682]\n",
      "b2 = [6.55311002e-07 1.43397035e-06] d2 = [0.41564523 0.90952682]\n",
      "[[-0.90952682  0.41564523]\n",
      " [ 0.41564523  0.90952682]]\n",
      "lmbda0: 7.655079554206775e-07 => y0: [2.1257893557302046, 1.0639352675225937]\n",
      "lmbda1: -0.0023037667256719315 => y1: [2.1248318060799036, 1.0618399298902486]\n",
      "lmbda^1: 0.0004944760891423623 => y3: [2.1248313322502184, 1.061838893953223] d:[-0.0009582459003198984,-0.0020950194526150323]\n",
      "a1 = [-0.00095825 -0.00209502] d1 = [-0.41594743 -0.90938866]\n",
      "a2 = [-0.00095755 -0.00209534] d2 = [ 0.90938866 -0.41594743]\n",
      "b2 = [ 6.96144216e-07 -3.18411049e-07] d2 = [ 0.90938866 -0.41594743]\n",
      "[[-0.41594743 -0.90938866]\n",
      " [ 0.90938866 -0.41594743]]\n",
      "lmbda0: -1.1040215470043803e-07 => y0: [2.1248313781717107, 1.0618389943516906]\n",
      "lmbda1: -0.0017839048104438115 => y1: [2.1232091153655324, 1.06258100497184]\n",
      "lmbda^1: 0.00017842137496005972 => y3: [2.123208825927365, 1.0625811373803085] d:[-0.0016222168846860008,0.0007421110186169866]\n",
      "a1 = [-0.00162222  0.00074211] d1 = [-0.90936292  0.41600371]\n",
      "a2 = [-0.00162226  0.00074201] d2 = [-0.41600371 -0.90936292]\n",
      "b2 = [-4.59277057e-08 -1.00395625e-07] d2 = [-0.41600371 -0.90936292]\n",
      "[[-0.90936292  0.41600371]\n",
      " [-0.41600371 -0.90936292]]\n",
      "lmbda0: 8.874253328737219e-07 => y0: [2.123208018935676, 1.0625815065525381]\n",
      "lmbda1: 0.0021682908892851353 => y1: [2.1223060018840876, 1.0606097432251582]\n",
      "lmbda^1: 5.991678422519663e-05 => y3: [2.122305947789774, 1.06060962510556] d:[-0.0009028240432775725,-0.001971394155150241]\n",
      "a1 = [-0.00090282 -0.00197139] d1 = [-0.41637585 -0.90919258]\n",
      "a2 = [-0.00090202 -0.00197176] d2 = [ 0.90919258 -0.41637585]\n",
      "b2 = [ 8.06840461e-07 -3.69502449e-07] d2 = [ 0.90919258 -0.41637585]\n",
      "[[-0.41637585 -0.90919258]\n",
      " [ 0.90919258 -0.41637585]]\n",
      "lmbda0: -4.3362825410002037e-07 => y0: [2.122306128342108, 1.0606100193571515]\n",
      "lmbda1: -0.0016784223918065663 => y1: [2.120780119155653, 1.0613088739115268]\n",
      "lmbda^1: 0.00033689695141886686 => y3: [2.1207796051086376, 1.0613091094863178] d:[-0.0015258286341213179,0.0006992488059669455]\n",
      "a1 = [-0.00152583  0.00069925] d1 = [-0.90908498  0.41661073]\n",
      "a2 = [-0.00152601  0.00069885] d2 = [-0.41661073 -0.90908498]\n",
      "b2 = [-1.80654179e-07 -3.94204919e-07] d2 = [-0.41661073 -0.90908498]\n",
      "[[-0.90908498  0.41661073]\n",
      " [-0.41661073 -0.90908498]]\n",
      "lmbda0: 1.5479271175141537e-06 => y0: [2.120778197911348, 1.0613097543693686]\n",
      "lmbda1: 0.002045711663436523 => y1: [2.119925932476108, 1.0594500286267345]\n",
      "lmbda^1: 0.0007539286515127942 => y3: [2.1199252888678517, 1.059448627012409] d:[-0.0008536726325294453,-0.0018590808595833064]\n",
      "a1 = [-0.00085367 -0.00185908] d1 = [-0.41729849 -0.90876948]\n",
      "a2 = [-0.00085227 -0.00185973] d2 = [ 0.90876948 -0.41729849]\n",
      "b2 = [ 1.40670852e-06 -6.45947464e-07] d2 = [ 0.90876948 -0.41729849]\n",
      "[[-0.41729849 -0.90876948]\n",
      " [ 0.90876948 -0.41729849]]\n",
      "lmbda0: 7.141585381778282e-07 => y0: [2.119924990850572, 1.0594479780069248]\n",
      "lmbda1: -0.0015853610608811887 => y1: [2.1184842631015197, 1.0601095467837494]\n",
      "lmbda^1: 9.541364353598251e-05 => y3: [2.118484125608001, 1.0601096098445129] d:[-0.0014410257663319825,0.000660919771340307]\n",
      "a1 = [-0.00144103  0.00066092] d1 = [-0.90895737  0.41688907]\n",
      "a2 = [-0.00144073  0.00066157] d2 = [0.41688907 0.90895737]\n",
      "b2 = [2.97724861e-07 6.49139601e-07] d2 = [0.41688907 0.90895737]\n",
      "[[-0.90895737  0.41688907]\n",
      " [ 0.41688907  0.90895737]]\n",
      "lmbda0: 9.073454563072704e-07 => y0: [2.1184833008696615, 1.0601099881069198]\n",
      "lmbda1: -0.0019308377687207157 => y1: [2.117678355700634, 1.0583549388871034]\n",
      "lmbda^1: 0.001069514304800116 => y3: [2.1176774939181917, 1.0583530622414143] d:[-0.0008057699073669156,-0.0017546709574094432]\n",
      "a1 = [-0.00080577 -0.00175467] d1 = [-0.41731617 -0.90876136]\n",
      "a2 = [-0.00080495 -0.00175505] d2 = [ 0.90876136 -0.41731617]\n",
      "b2 = [ 8.24560403e-07 -3.78649887e-07] d2 = [ 0.90876136 -0.41731617]\n",
      "[[-0.41731617 -0.90876136]\n",
      " [ 0.90876136 -0.41731617]]\n",
      "lmbda0: 6.528258193776667e-07 => y0: [2.1176772214834223, 1.0583524689785324]\n",
      "lmbda1: -0.0014994477019624219 => y1: [2.1163145813450805, 1.0589782127474192]\n",
      "lmbda^1: -0.00014921918083349433 => y3: [2.1163147847177783, 1.0589781194629728] d:[-0.0013629125731111635,0.0006251505060048679]\n",
      "a1 = [-0.00136291  0.00062515] d1 = [-0.90894297  0.41692047]\n",
      "a2 = [-0.00136264  0.00062574] d2 = [0.41692047 0.90894297]\n",
      "b2 = [2.72176424e-07 5.93381381e-07] d2 = [0.41692047 0.90894297]\n",
      "[[-0.90894297  0.41692047]\n",
      " [ 0.41692047  0.90894297]]\n",
      "lmbda0: -5.465985206780047e-08 => y0: [2.1163148344004665, 1.0589780966741613]\n",
      "lmbda1: -0.0018275577641802677 => y1: [2.1155528881510217, 1.0573169508965812]\n",
      "lmbda^1: -0.00029133822901854413 => y3: [2.115553110120618, 1.0573174348584895] d:[-0.0007618965667566258,-0.0016611685663916465]\n",
      "a1 = [-0.0007619  -0.00166117] d1 = [-0.41689329 -0.90895544]\n",
      "a2 = [-0.00076195 -0.00166115] d2 = [-0.90895544  0.41689329]\n",
      "b2 = [-4.96833697e-08  2.27873255e-08] d2 = [-0.90895544  0.41689329]\n",
      "[[-0.41689329 -0.90895544]\n",
      " [-0.90895544  0.41689329]]\n",
      "lmbda0: 6.793634093108221e-07 => y0: [2.1155528268985724, 1.0573168173474252]\n",
      "lmbda1: 0.0014205685380479242 => y1: [2.114261593402695, 1.057909042836927]\n",
      "lmbda^1: 0.0002204296695387767 => y3: [2.1142613087140916, 1.0579091732448782] d:[-0.001291516717923269,0.0005916079784376027]\n",
      "a1 = [-0.00129152  0.00059161] d1 = [-0.90915471  0.41645855]\n",
      "a2 = [-0.00129123  0.00059223] d2 = [0.41645855 0.90915471]\n",
      "b2 = [2.82926667e-07 6.17646369e-07] d2 = [0.41645855 0.90915471]\n",
      "[[-0.90915471  0.41645855]\n",
      " [ 0.41645855  0.90915471]]\n",
      "lmbda0: 3.361300705947108e-07 => y0: [2.1142610031198563, 1.0579093132291193]\n",
      "lmbda1: -0.0017297746939531997 => y1: [2.1135406236623306, 1.0563366804274088]\n",
      "lmbda^1: 0.0012227714823041527 => y3: [2.1135397424292015, 1.0563347576280355] d:[-0.0007206850517609276,-0.0015724928174694064]\n",
      "a1 = [-0.00072069 -0.00157249] d1 = [-0.41663521 -0.90907376]\n",
      "a2 = [-0.00072038 -0.00157263] d2 = [ 0.90907376 -0.41663521]\n",
      "b2 = [ 3.05567022e-07 -1.40043619e-07] d2 = [ 0.90907376 -0.41663521]\n",
      "[[-0.41663521 -0.90907376]\n",
      " [ 0.90907376 -0.41663521]]\n",
      "lmbda0: 7.169115176913716e-07 => y0: [2.1135394437386226, 1.0563341059025855]\n",
      "lmbda1: -0.001347863516851814 => y1: [2.112314136381212, 1.0568956732983057]\n",
      "lmbda^1: 0.0009216381330432052 => y3: [2.1123130068159424, 1.0568961902595768] d:[-0.0012256060479893804,0.0005609156702701679]\n",
      "a1 = [-0.00122561  0.00056092] d1 = [-0.90929524  0.41615162]\n",
      "a2 = [-0.00122531  0.00056157] d2 = [0.41615162 0.90929524]\n",
      "b2 = [2.98343850e-07 6.51884135e-07] d2 = [0.41615162 0.90929524]\n",
      "[[-0.90929524  0.41615162]\n",
      " [ 0.41615162  0.90929524]]\n",
      "lmbda0: 9.406114633451017e-07 => y0: [2.1123121515224197, 1.0568965816965645]\n",
      "lmbda1: -0.001643945566888607 => y1: [2.111628020905651, 1.0554017498243822]\n",
      "lmbda^1: 0.0011223614173944026 => y3: [2.1116272521038937, 1.055400072522097] d:[-0.0006849859102913136,-0.0014944404351946439]\n",
      "a1 = [-0.00068499 -0.00149444] d1 = [-0.41667182 -0.90905698]\n",
      "a2 = [-0.00068413 -0.00149483] d2 = [ 0.90905698 -0.41667182]\n",
      "b2 = [ 8.55069275e-07 -3.91926230e-07] d2 = [ 0.90905698 -0.41667182]\n",
      "[[-0.41667182 -0.90905698]\n",
      " [ 0.90905698 -0.41667182]]\n",
      "lmbda0: -1.9183604856143156e-07 => y0: [2.11162733203657, 1.0554002469119959]\n",
      "lmbda1: -0.0012808794475597223 => y1: [2.110462939635588, 1.0559339532879333]\n",
      "lmbda^1: 0.0005319669154278343 => y3: [2.1104623202598756, 1.0559342372948375] d:[-0.0011643124683056172,0.0005338807658361588]\n",
      "a1 = [-0.00116431  0.00053388] d1 = [-0.90899456  0.41680797]\n",
      "a2 = [-0.00116439  0.00053371] d2 = [-0.41680797 -0.90899456]\n",
      "b2 = [-7.99587927e-08 -1.74377923e-07] d2 = [-0.41680797 -0.90899456]\n",
      "[[-0.90899456  0.41680797]\n",
      " [-0.41680797 -0.90899456]]\n",
      "lmbda0: 1.442835202635583e-06 => y0: [2.1104610087305193, 1.0559348386800465]\n",
      "lmbda1: 0.001568482483319697 => y1: [2.1098072527337304, 1.054509096628642]\n",
      "lmbda^1: 0.0005136795870097268 => y3: [2.109806916238914, 1.0545083645629731] d:[-0.0006550675261451921,-0.00142514066619559]\n",
      "a1 = [-0.00065507 -0.00142514] d1 = [-0.41764397 -0.90861076]\n",
      "a2 = [-0.00065376 -0.00142574] d2 = [ 0.90861076 -0.41764397]\n",
      "b2 = [ 1.31097504e-06 -6.02591165e-07] d2 = [ 0.90861076 -0.41764397]\n",
      "[[-0.41764397 -0.90861076]\n",
      " [ 0.90861076 -0.41764397]]\n",
      "lmbda0: -1.9975072247802468e-08 => y0: [2.1098069245813824, 1.0545083827125388]\n",
      "lmbda1: -0.0012205742653098226 => y1: [2.108697897668985, 1.0550181481926526]\n",
      "lmbda^1: -6.351132245982269e-05 => y3: [2.108697968104221, 1.0550181158156202] d:[-0.0011090185699287325,0.0005097836296794611]\n",
      "a1 = [-0.00110902  0.00050978] d1 = [-0.90860393  0.41765884]\n",
      "a2 = [-0.00110903  0.00050977] d2 = [-0.41765884 -0.90860393]\n",
      "b2 = [-8.34276547e-09 -1.81494291e-08] d2 = [-0.41765884 -0.90860393]\n",
      "[[-0.90860393  0.41765884]\n",
      " [-0.41765884 -0.90860393]]\n",
      "lmbda0: 1.3116696141136584e-06 => y0: [2.1086967763160596, 1.0550186636460275]\n",
      "lmbda1: 0.0014960094212725019 => y1: [2.108071954759195, 1.0536593836121075]\n",
      "lmbda^1: 0.0011575036910871721 => y3: [2.1080712301464377, 1.0536578108745667] d:[-0.0006260133450259886,-0.0013587322035126803]\n",
      "a1 = [-0.00062601 -0.00135873] d1 = [-0.41845532 -0.90823738]\n",
      "a2 = [-0.00062482 -0.00135928] d2 = [ 0.90823738 -0.41845532]\n",
      "b2 = [ 1.19130692e-06 -5.48874920e-07] d2 = [ 0.90823738 -0.41845532]\n",
      "[[-0.41845532 -0.90823738]\n",
      " [ 0.90823738 -0.41845532]]\n",
      "lmbda0: 1.3408643352524409e-06 => y0: [2.1080706690546203, 1.0536565930514523]\n",
      "lmbda1: -0.0011641630944424976 => y1: [2.1070133326126994, 1.05414374329424]\n",
      "lmbda^1: 0.001592220385281026 => y3: [2.1070116482066807, 1.0541445170057444] d:[-0.0010578975337383056,0.00048593241967331835]\n",
      "a1 = [-0.0010579   0.00048593] d1 = [-0.90871875  0.41740895]\n",
      "a2 = [-0.00105734  0.00048715] d2 = [0.41740895 0.90871875]\n",
      "b2 = [5.59688406e-07 1.21846775e-06] d2 = [0.41740895 0.90871875]\n",
      "[[-0.90871875  0.41740895]\n",
      " [ 0.41740895  0.90871875]]\n",
      "lmbda0: 1.426406255933002e-06 => y0: [2.1070103520045707, 1.054145112400485]\n",
      "lmbda1: -0.0014257341796904302 => y1: [2.1064152377946184, 1.0528495210188178]\n",
      "lmbda^1: 0.002410689325261985 => y3: [2.1064138000344044, 1.052846399185816] d:[-0.0005964104120623759,-0.001294995986926617]\n",
      "a1 = [-0.00059641 -0.001295  ] d1 = [-0.41831789 -0.90830069]\n",
      "a2 = [-0.00059511 -0.00129559] d2 = [ 0.90830069 -0.41831789]\n",
      "b2 = [ 1.29560514e-06 -5.96690956e-07] d2 = [ 0.90830069 -0.41831789]\n",
      "[[-0.41831789 -0.90830069]\n",
      " [ 0.90830069 -0.41831789]]\n",
      "lmbda0: 1.2327087762430402e-06 => y0: [2.1064132843702703, 1.0528452795155843]\n",
      "lmbda1: -0.0011128127242486297 => y1: [2.1054025158052685, 1.053310788986222]\n",
      "lmbda^1: 0.0008734079251846309 => y3: [2.105401632541608, 1.053311194587954] d:[-0.0010112842291358781,0.00046438980040619526]\n",
      "a1 = [-0.00101128  0.00046439] d1 = [-0.90876352  0.41731147]\n",
      "a2 = [-0.00101077  0.00046551] d2 = [0.41731147 0.90876352]\n",
      "b2 = [5.14423198e-07 1.12024008e-06] d2 = [0.41731147 0.90876352]\n",
      "[[-0.90876352  0.41731147]\n",
      " [ 0.41731147  0.90876352]]\n",
      "lmbda0: 1.6273956219557729e-06 => y0: [2.1054001536238336, 1.0533118737188158]\n",
      "lmbda1: -0.001364059525307244 => y1: [2.104830915936087, 1.0520722661827664]\n",
      "lmbda^1: 0.001626790741259101 => y3: [2.1048299874995973, 1.0520702507055077] d:[-0.0005707166055208823,-0.0012389284051876892]\n",
      "a1 = [-0.00057072 -0.00123893] d1 = [-0.41839538 -0.908265  ]\n",
      "a2 = [-0.00056924 -0.00123961] d2 = [ 0.908265   -0.41839538]\n",
      "b2 = [ 1.47810543e-06 -6.80894320e-07] d2 = [ 0.908265   -0.41839538]\n",
      "[[-0.41839538 -0.908265  ]\n",
      " [ 0.908265   -0.41839538]]\n",
      "lmbda0: -5.065395371944105e-07 => y0: [2.104830199433398, 1.05207071077764]\n",
      "lmbda1: -0.001062042357930776 => y1: [2.1038655835321802, 1.0525150643903822]\n",
      "lmbda^1: 0.0006133766300637384 => y3: [2.1038649919893246, 1.0525153372287013] d:[-0.0009644039674170379,0.0004448136848744788]\n",
      "a1 = [-0.0009644   0.00044481] d1 = [-0.90806534  0.41882853]\n",
      "a2 = [-0.00096462  0.00044435] d2 = [-0.41882853 -0.90806534]\n",
      "b2 = [-2.12153183e-07 -4.59970946e-07] d2 = [-0.41882853 -0.90806534]\n",
      "[[-0.90806534  0.41882853]\n",
      " [-0.41882853 -0.90806534]]\n",
      "lmbda0: 1.5232039392340306e-06 => y0: [2.1038636088206175, 1.0525159751899604]\n",
      "lmbda1: 0.0013096595041751594 => y1: [2.1033150860622047, 1.0513267187834696]\n",
      "lmbda^1: 0.0011329652773268155 => y3: [2.1033144630378837, 1.0513253721200433] d:[-0.000549905927119898,-0.0011886184452316595]\n",
      "a1 = [-0.00054991 -0.00118862] d1 = [-0.41988437 -0.90757761]\n",
      "a2 = [-0.00054852 -0.00118926] d2 = [ 0.90757761 -0.41988437]\n",
      "b2 = [ 1.38242485e-06 -6.39569093e-07] d2 = [ 0.90757761 -0.41988437]\n",
      "[[-0.41988437 -0.90757761]\n",
      " [ 0.90757761 -0.41988437]]\n",
      "lmbda0: 1.543425260700003e-06 => y0: [2.1033138149777413, 1.0513239713418356]\n",
      "lmbda1: -0.0010197981815113651 => y1: [2.102388268982572, 1.051752168658315]\n",
      "lmbda^1: 0.0006031090181820158 => y3: [2.102387710386585, 1.051752426063156] d:[-0.0009261940553115799,0.000426796538271601]\n",
      "a1 = [-0.00092619  0.0004268 ] d1 = [-0.90821205  0.41851031]\n",
      "a2 = [-0.00092555  0.0004282 ] d2 = [0.41851031 0.90821205]\n",
      "b2 = [6.45938639e-07 1.40175581e-06] d2 = [0.41851031 0.90821205]\n",
      "[[-0.90821205  0.41851031]\n",
      " [ 0.41851031  0.90821205]]\n",
      "lmbda0: 3.8516386514132854e-07 => y0: [2.102387360576122, 1.0517525872582034]\n",
      "lmbda1: -0.0012527365335437044 => y1: [2.101863077425558, 1.0506148368459822]\n",
      "lmbda^1: 6.753774269277005e-05 => y3: [2.101863041993032, 1.0506147600157745] d:[-0.0005246329610271161,-0.0011375892171738844]\n",
      "a1 = [-0.00052463 -0.00113759] d1 = [-0.41878952 -0.90808333]\n",
      "a2 = [-0.00052428 -0.00113775] d2 = [ 0.90808333 -0.41878952]\n",
      "b2 = [ 3.49760869e-07 -1.61302584e-07] d2 = [ 0.90808333 -0.41878952]\n",
      "[[-0.41878952 -0.90808333]\n",
      " [ 0.90808333 -0.41878952]]\n",
      "lmbda0: 6.602610332049272e-07 => y0: [2.1018627654826285, 1.0506141604437365]\n",
      "lmbda1: -0.0009754125832827214 => y1: [2.1009770095755558, 1.0510226530149198]\n",
      "lmbda^1: 0.0021965756617081302 => y3: [2.100975063338312, 1.0510235489827544] d:[-0.0008860324174762013,0.00040789299914534283]\n",
      "a1 = [-0.00088603  0.00040789] d1 = [-0.9083666   0.41817474]\n",
      "a2 = [-0.00088576  0.00040849] d2 = [0.41817474 0.9083666 ]\n",
      "b2 = [2.76104424e-07 5.99758934e-07] d2 = [0.41817474 0.9083666 ]\n",
      "[[-0.9083666   0.41817474]\n",
      " [ 0.41817474  0.9083666 ]]\n",
      "lmbda0: 9.51194416073226e-07 => y0: [2.100974199305072, 1.0510239467482343]\n",
      "lmbda1: -0.0012006055553815047 => y1: [2.100472136386289, 1.049933356758735]\n",
      "lmbda^1: 0.0016255791482943692 => y3: [2.1004713188387223, 1.049931584564988] d:[-0.0005029269520231416,-0.001090192224019404]\n",
      "a1 = [-0.00050293 -0.00109019] d1 = [-0.41889428 -0.90803501]\n",
      "a2 = [-0.00050206 -0.00109059] d2 = [ 0.90803501 -0.41889428]\n",
      "b2 = [ 8.63717564e-07 -3.98449771e-07] d2 = [ 0.90803501 -0.41889428]\n",
      "[[-0.41889428 -0.90803501]\n",
      " [ 0.90803501 -0.41889428]]\n",
      "lmbda0: -3.187442095994598e-07 => y0: [2.100471452358847, 1.0499318739958907]\n",
      "lmbda1: -0.0009357519647904119 => y1: [2.0996217568107216, 1.050323855137153]\n",
      "lmbda^1: 0.0012552372824325946 => y3: [2.0996206904087904, 1.0503243475297999] d:[-0.0008495620280006833,0.00039227057216506545]\n",
      "a1 = [-0.00084956  0.00039227] d1 = [-0.90789227  0.41920355]\n",
      "a2 = [-0.0008497   0.00039198] d2 = [-0.41920355 -0.90789227]\n",
      "b2 = [-1.33618698e-07 -2.89385388e-07] d2 = [-0.41920355 -0.90789227]\n",
      "[[-0.90789227  0.41920355]\n",
      " [-0.41920355 -0.90789227]]\n",
      "lmbda0: 9.669219233203187e-07 => y0: [2.0996198125478474, 1.0503247528669066]\n",
      "lmbda1: 0.0011558797265449558 => y1: [2.099135263658353, 1.0492753385941205]\n",
      "lmbda^1: 0.0012183136092809097 => y3: [2.0991346722563367, 1.049274060572258] d:[-0.00048542675043750094,-0.0010490089356793852]\n",
      "a1 = [-0.00048543 -0.00104901] d1 = [-0.41996288 -0.90754128]\n",
      "a2 = [-0.00048455 -0.00104941] d2 = [ 0.90754128 -0.41996288]\n",
      "b2 = [ 8.77521255e-07 -4.06071175e-07] d2 = [ 0.90754128 -0.41996288]\n",
      "[[-0.41996288 -0.90754128]\n",
      " [ 0.90754128 -0.41996288]]\n",
      "lmbda0: 9.990822492897422e-07 => y0: [2.0991342526788763, 1.0492731538638727]\n",
      "lmbda1: -0.0009006683984181355 => y1: [2.098316858926091, 1.0496514011597218]\n",
      "lmbda^1: 0.0016035576087362192 => y3: [2.098315547515303, 1.049652006247092] d:[-0.0008178133302454427,0.0003773405874638591]\n",
      "a1 = [-0.00081781  0.00037734] d1 = [-0.90800657  0.41895592]\n",
      "a2 = [-0.00081739  0.00037825] d2 = [0.41895592 0.90800657]\n",
      "b2 = [4.18571163e-07 9.07172693e-07] d2 = [0.41895592 0.90800657]\n",
      "[[-0.90800657  0.41895592]\n",
      " [ 0.41895592  0.90800657]]\n",
      "lmbda0: 5.696975257125542e-07 => y0: [2.098315030226204, 1.0496522449252417]\n",
      "lmbda1: -0.0011094513116116655 => y1: [2.0978502190339183, 1.0486448558403483]\n",
      "lmbda^1: 0.001537462551308502 => y3: [2.097849503608804, 1.0486433073843144] d:[-0.0004653284813844749,-0.0010071504067437065]\n",
      "a1 = [-0.00046533 -0.00100715] d1 = [-0.41942212 -0.90779132]\n",
      "a2 = [-0.00046481 -0.00100739] d2 = [ 0.90779132 -0.41942212]\n",
      "b2 = [ 5.17166402e-07 -2.38943712e-07] d2 = [ 0.90779132 -0.41942212]\n",
      "[[-0.41942212 -0.90779132]\n",
      " [ 0.90779132 -0.41942212]]\n",
      "lmbda0: 6.44859954239497e-07 => y0: [2.0978492331402756, 1.0486427219860435]\n",
      "lmbda1: -0.0008664092555630018 => y1: [2.097062714335916, 1.049006113191928]\n",
      "lmbda^1: 0.0012529908162942044 => y3: [2.0970617284961826, 1.049006567784273] d:[-0.000786789272888111,0.0003628058076134977]\n",
      "a1 = [-0.00078679  0.00036281] d1 = [-0.90810324  0.41874634]\n",
      "a2 = [-0.00078652  0.00036339] d2 = [0.41874634 0.90810324]\n",
      "b2 = [2.70032673e-07 5.85599254e-07] d2 = [0.41874634 0.90810324]\n",
      "[[-0.90810324  0.41874634]\n",
      " [ 0.41874634  0.90810324]]\n",
      "lmbda0: 2.849834144219956e-07 => y0: [2.0970614697018197, 1.0490066871200356]\n",
      "lmbda1: -0.0010678491663759543 => y1: [2.0966143117687537, 1.048036969828746]\n",
      "lmbda^1: 0.0003897771769431147 => y3: [2.0966141373759246, 1.0480365919015922] d:[-0.0004474167274288732,-0.0009695979555270906]\n",
      "a1 = [-0.00044742 -0.0009696 ] d1 = [-0.41898868 -0.90799146]\n",
      "a2 = [-0.00044716 -0.00096972] d2 = [ 0.90799146 -0.41898868]\n",
      "b2 = [ 2.58762497e-07 -1.19404820e-07] d2 = [ 0.90799146 -0.41898868]\n",
      "[[-0.41898868 -0.90799146]\n",
      " [ 0.90799146 -0.41898868]]\n",
      "lmbda0: 4.500643726146203e-07 => y0: [2.096613948804048, 1.0480361832469864]\n",
      "lmbda1: -0.0008354310321177722 => y1: [2.0958553845635306, 1.0483862193914544]\n",
      "lmbda^1: -0.00024949813409239617 => y3: [2.0958555738709417, 1.048386132160048] d:[-0.0007587528123940146,0.00034962748986222536]\n",
      "a1 = [-0.00075875  0.00034963] d1 = [-0.90821704  0.41849946]\n",
      "a2 = [-0.00075856  0.00035004] d2 = [0.41849946 0.90821704]\n",
      "b2 = [1.88351671e-07 4.08756075e-07] d2 = [0.41849946 0.90821704]\n",
      "[[-0.90821704  0.41849946]\n",
      " [ 0.41849946  0.90821704]]\n",
      "lmbda0: 1.1584275272237901e-06 => y0: [2.0958545217673175, 1.0483866169613472]\n",
      "lmbda1: -0.0010289027425692864 => y1: [2.0954239265210717, 1.04745214995413]\n",
      "lmbda^1: 0.0018209002277565925 => y3: [2.0954231405343138, 1.0474504492657186] d:[-0.0004316473498700013,-0.0009339822059180047]\n",
      "a1 = [-0.00043165 -0.00093398] d1 = [-0.41952175 -0.90774529]\n",
      "a2 = [-0.0004306  -0.00093447] d2 = [ 0.90774529 -0.41952175]\n",
      "b2 = [ 1.05155646e-06 -4.85985232e-07] d2 = [ 0.90774529 -0.41952175]\n",
      "[[-0.41952175 -0.90774529]\n",
      " [ 0.90774529 -0.41952175]]\n",
      "lmbda0: 5.7043929394296006e-08 => y0: [2.0954231166031447, 1.0474503974843605]\n",
      "lmbda1: -0.0008036310760943259 => y1: [2.094693624282425, 1.0477875381975283]\n",
      "lmbda^1: 0.0007927183802700661 => y3: [2.0946930459814834, 1.0477878054141203] d:[-0.0007295162518889953,0.0003370889318097259]\n",
      "a1 = [-0.00072952  0.00033709] d1 = [-0.90777506  0.41945731]\n",
      "a2 = [-0.00072949  0.00033714] d2 = [0.41945731 0.90777506]\n",
      "b2 = [2.39274932e-08 5.17830564e-08] d2 = [0.41945731 0.90777506]\n",
      "[[-0.90777506  0.41945731]\n",
      " [ 0.41945731  0.90777506]]\n",
      "lmbda0: 1.2168152834341802e-07 => y0: [2.0946929355220263, 1.047787856454327]\n",
      "lmbda1: -0.0009926839729914561 => y1: [2.094276546971121, 1.0468867226990446]\n",
      "lmbda^1: 0.0004516334884029326 => y3: [2.09427635886622, 1.0468863157399146] d:[-0.0004164990103623367,-0.0009010827150757184]\n",
      "a1 = [-0.0004165  -0.00090108] d1 = [-0.41956858 -0.90772364]\n",
      "a2 = [-0.00041639 -0.00090113] d2 = [ 0.90772364 -0.41956858]\n",
      "b2 = [ 1.10453199e-07 -5.10537460e-08] d2 = [ 0.90772364 -0.41956858]\n",
      "[[-0.41956858 -0.90772364]\n",
      " [ 0.90772364 -0.41956858]]\n",
      "lmbda0: -8.935274698246954e-08 => y0: [2.0942763963558253, 1.0468863968475153]\n",
      "lmbda1: -0.0007756516763316249 => y1: [2.093572318993616, 1.0472118359217217]\n",
      "lmbda^1: -8.130839097095868e-06 => y3: [2.0935723247180507, 1.0472118332749696] d:[-0.0007040398726041452,0.00032552018180709297]\n",
      "a1 = [-0.00070404  0.00032552] d1 = [-0.9076753   0.41967315]\n",
      "a2 = [-0.00070408  0.00032544] d2 = [-0.41967315 -0.9076753 ]\n",
      "b2 = [-3.74989482e-08 -8.11032809e-08] d2 = [-0.41967315 -0.9076753 ]\n",
      "[[-0.9076753   0.41967315]\n",
      " [-0.41967315 -0.9076753 ]]\n",
      "lmbda0: 4.3000747236115506e-07 => y0: [2.0935719344108894, 1.0472120137375585]\n",
      "lmbda1: 0.0009590722304891238 => y1: [2.0931694375501326, 1.0463414875631236]\n",
      "lmbda^1: 0.0004933030172821942 => y3: [2.093169238804677, 1.0463410582189578] d:[-0.00040288716791803125,-0.0008703457118459657]\n",
      "a1 = [-0.00040289 -0.00087035] d1 = [-0.42008007 -0.90748704]\n",
      "a2 = [-0.0004025  -0.00087053] d2 = [ 0.90748704 -0.42008007]\n",
      "b2 = [ 3.90226171e-07 -1.80637550e-07] d2 = [ 0.90748704 -0.42008007]\n",
      "[[-0.42008007 -0.90748704]\n",
      " [ 0.90748704 -0.42008007]]\n",
      "lmbda0: -4.3663057815665064e-07 => y0: [2.09316942222448, 1.046341454455551]\n",
      "lmbda1: -0.0007485452755404678 => y1: [2.0924901270843486, 1.0466559034055063]\n",
      "lmbda^1: -0.00045559594557888705 => y3: [2.092490436484895, 1.0466557599633157] d:[-0.000679111720328418,0.0003148451865484514]\n",
      "a1 = [-0.00067911  0.00031485] d1 = [-0.90724186  0.42060934]\n",
      "a2 = [-0.0006793   0.00031445] d2 = [-0.42060934 -0.90724186]\n",
      "b2 = [-1.83650867e-07 -3.96129469e-07] d2 = [-0.42060934 -0.90724186]\n",
      "[[-0.90724186  0.42060934]\n",
      " [-0.42060934 -0.90724186]]\n",
      "lmbda0: -2.911240117934573e-07 => y0: [2.0924907006047837, 1.0466556375138378]\n",
      "lmbda1: 0.0009266701877276143 => y1: [2.0921009344703587, 1.045814923533177]\n",
      "lmbda^1: -7.469187651147987e-05 => y3: [2.092100963562995, 1.0458149863368278] d:[-0.00038950201453635813,-0.0008408364301386584]\n",
      "a1 = [-0.0003895  -0.00084084] d1 = [-0.4203243  -0.90737395]\n",
      "a2 = [-0.00038977 -0.00084071] d2 = [-0.90737395  0.4203243 ]\n",
      "b2 = [-2.64158332e-07  1.22366490e-07] d2 = [-0.90737395  0.4203243 ]\n",
      "[[-0.4203243  -0.90737395]\n",
      " [-0.90737395  0.4203243 ]]\n",
      "lmbda0: 1.3704327243073528e-07 => y0: [2.0921009059603777, 1.0458148619873324]\n",
      "lmbda1: 0.0007234116237237948 => y1: [2.0914445010978375, 1.0461189294695996]\n",
      "lmbda^1: 0.000537322731164973 => y3: [2.0914441483656327, 1.0461190927851538] d:[-0.0006564624651574924,0.0003039431327718223]\n",
      "a1 = [-0.00065646  0.00030394] d1 = [-0.90745356  0.4201524 ]\n",
      "a2 = [-0.0006564   0.00030407] d2 = [0.4201524  0.90745356]\n",
      "b2 = [5.75790583e-08 1.24360403e-07] d2 = [0.4201524  0.90745356]\n",
      "[[-0.90745356  0.4201524 ]\n",
      " [ 0.4201524   0.90745356]]\n",
      "lmbda0: -1.9925749855055122e-07 => y0: [2.091444329182559, 1.0461190090666384]\n",
      "lmbda1: -0.000894772684710293 => y1: [2.0910683882948122, 1.0453070444084027]\n",
      "lmbda^1: 0.0003082362727218011 => y3: [2.0910682724719285, 1.0453067941056378] d:[-0.0003757600708205189,-0.0008120483767510844]\n",
      "a1 = [-0.00037576 -0.00081205] d1 = [-0.4199503 -0.9075471]\n",
      "a2 = [-0.00037594 -0.00081196] d2 = [-0.9075471  0.4199503]\n",
      "b2 = [-1.80835561e-07  8.36782451e-08] d2 = [-0.9075471  0.4199503]\n",
      "[[-0.4199503 -0.9075471]\n",
      " [-0.9075471  0.4199503]]\n",
      "lmbda0: 1.5077518426048419e-06 => y0: [2.091067639291083, 1.045305425749823]\n",
      "lmbda1: 0.0007015940751355176 => y1: [2.090430909621681, 1.0456000603953903]\n",
      "lmbda^1: 0.0008248308271273061 => y3: [2.0904303839051543, 1.0456003022904667] d:[-0.0006373628502474027,0.0002932662897525695]\n",
      "a1 = [-0.00063736  0.00029327] d1 = [-0.90844749  0.41799899]\n",
      "a2 = [-0.00063673  0.00029463] d2 = [0.41799899 0.90844749]\n",
      "b2 = [6.30237287e-07 1.36971022e-06] d2 = [0.41799899 0.90844749]\n",
      "[[-0.90844749  0.41799899]\n",
      " [ 0.41799899  0.90844749]]\n",
      "lmbda0: 1.1361475700376938e-06 => y0: [2.090429351774743, 1.045600777199]\n",
      "lmbda1: -0.0008624553795859033 => y1: [2.0900688462999177, 1.0448172817718289]\n",
      "lmbda^1: 0.003594531489596798 => y3: [2.090067546741611, 1.0448144671799175] d:[-0.00036153760523660594,-0.000783020518637878]\n",
      "a1 = [-0.00036154 -0.00078302] d1 = [-0.41919536 -0.90789606]\n",
      "a2 = [-0.00036051 -0.0007835 ] d2 = [ 0.90789606 -0.41919536]\n",
      "b2 = [ 1.03150300e-06 -4.76267374e-07] d2 = [ 0.90789606 -0.41919536]\n",
      "[[-0.41919536 -0.90789606]\n",
      " [ 0.90789606 -0.41919536]]\n",
      "lmbda0: 5.670526637352762e-08 => y0: [2.0900675229710264, 1.0448144156974297]\n",
      "lmbda1: -0.0006776855653616325 => y1: [2.089452254917757, 1.045098498340557]\n",
      "lmbda^1: -0.00033574781020126844 => y3: [2.0894524615006396, 1.0450984029777166] d:[-0.0006152918238537808,0.0002840311606393531]\n",
      "a1 = [-0.00061529  0.00028403] d1 = [-0.90793113  0.41911939]\n",
      "a2 = [-0.00061527  0.00028408] d2 = [0.41911939 0.90793113]\n",
      "b2 = [2.37662765e-08 5.14844764e-08] d2 = [0.41911939 0.90793113]\n",
      "[[-0.90793113  0.41911939]\n",
      " [ 0.41911939  0.90793113]]\n",
      "lmbda0: 1.9497039560338488e-08 => y0: [2.0894524437986703, 1.0450984111493038]\n",
      "lmbda1: -0.0008357718765145185 => y1: [2.0891021556009908, 1.0443395878443513]\n",
      "lmbda^1: 0.0013592603200043053 => y3: [2.0891016794440813, 1.0443385564170504] d:[-0.0003503058996487951,-0.000758815133365287]\n",
      "a1 = [-0.00035031 -0.00075882] d1 = [-0.41914057 -0.90792135]\n",
      "a2 = [-0.00035029 -0.00075882] d2 = [ 0.90792135 -0.41914057]\n",
      "b2 = [ 1.77017785e-08 -8.17200025e-09] d2 = [ 0.90792135 -0.41914057]\n",
      "[[-0.41914057 -0.90792135]\n",
      " [ 0.90792135 -0.41914057]]\n",
      "lmbda0: 1.214661485306719e-06 => y0: [2.0891011703301756, 1.044337453599951]\n",
      "lmbda1: -0.0006562791639215295 => y1: [2.0885053204635393, 1.0446125268218887]\n",
      "lmbda^1: 0.002485681769747323 => y3: [2.0885038381048933, 1.0446132078251293] d:[-0.000596358980542,0.00027397040483823076]\n",
      "a1 = [-0.00059636  0.00027397] d1 = [-0.90869556  0.41745945]\n",
      "a2 = [-0.00059585  0.00027507] d2 = [0.41745945 0.90869556]\n",
      "b2 = [5.07071041e-07 1.10375560e-06] d2 = [0.41745945 0.90869556]\n",
      "[[-0.90869556  0.41745945]\n",
      " [ 0.41745945  0.90869556]]\n",
      "lmbda0: 1.0277970943782324e-06 => y0: [2.0885029041502423, 1.044613636888734]\n",
      "lmbda1: -0.0008098594520279606 => y1: [2.0881648206728083, 1.043877721204484]\n",
      "lmbda^1: 0.0009843614698769277 => y3: [2.0881644869571105, 1.0438769972197932] d:[-0.0003390174320849759,-0.0007354866206452648]\n",
      "a1 = [-0.00033902 -0.00073549] d1 = [-0.41861234 -0.90816502]\n",
      "a2 = [-0.00033808 -0.00073592] d2 = [ 0.90816502 -0.41861234]\n",
      "b2 = [ 9.33408621e-07 -4.30248199e-07] d2 = [ 0.90816502 -0.41861234]\n",
      "[[-0.41861234 -0.90816502]\n",
      " [ 0.90816502 -0.41861234]]\n",
      "lmbda0: 3.6637441446637776e-07 => y0: [2.08816433358826, 1.0438766644913644]\n",
      "lmbda1: -0.0006360933315381287 => y1: [2.087586655872824, 1.044142941008384]\n",
      "lmbda^1: 0.0004514162276978275 => y3: [2.087586395030496, 1.0441430610597258] d:[-0.0005778310842865508,0.0002659437885907945]\n",
      "a1 = [-0.00057783  0.00026594] d1 = [-0.90840598  0.41808919]\n",
      "a2 = [-0.00057768  0.00026628] d2 = [0.41808919 0.90840598]\n",
      "b2 = [1.53177156e-07 3.32816655e-07] d2 = [0.41808919 0.90840598]\n",
      "[[-0.90840598  0.41808919]\n",
      " [ 0.41808919  0.90840598]]\n",
      "lmbda0: 9.669885067179428e-07 => y0: [2.0875855166123505, 1.0441434653471653]\n",
      "lmbda1: -0.000785768556724576 => y1: [2.0872569952745104, 1.0434296684885838]\n",
      "lmbda^1: 0.0010694643687895154 => y3: [2.0872566429932085, 1.043428905540648] d:[-0.0003293997559854489,-0.0007133925711420019]\n",
      "a1 = [-0.0003294  -0.00071339] d1 = [-0.41920678 -0.90789078]\n",
      "a2 = [-0.00032852 -0.0007138 ] d2 = [ 0.90789078 -0.41920678]\n",
      "b2 = [ 8.77919289e-07 -4.05367831e-07] d2 = [ 0.90789078 -0.41920678]\n",
      "[[-0.41920678 -0.90789078]\n",
      " [ 0.90789078 -0.41920678]]\n",
      "lmbda0: -2.4606729460104506e-07 => y0: [2.0872567461462865, 1.043429128942877]\n",
      "lmbda1: -0.0006159414349562963 => y1: [2.0866975385940543, 1.0436873357685466]\n",
      "lmbda^1: 0.00010174071168794408 => y3: [2.0866974817103747, 1.0436873620614218] d:[-0.0005591043991541866,0.00025843022789850956]\n",
      "a1 = [-0.0005591   0.00025843] d1 = [-0.90772324  0.41956945]\n",
      "a2 = [-0.00055921  0.00025821] d2 = [-0.41956945 -0.90772324]\n",
      "b2 = [-1.03242310e-07 -2.23360984e-07] d2 = [-0.41956945 -0.90772324]\n",
      "[[-0.90772324  0.41956945]\n",
      " [-0.41956945 -0.90772324]]\n",
      "lmbda0: 3.2325648850985715e-07 => y0: [2.086697188282948, 1.043687497689968]\n",
      "lmbda1: 0.0007634569015103321 => y1: [2.086376865092939, 1.0429944901182875]\n",
      "lmbda^1: 8.784288777803566e-05 => y3: [2.0863768369290496, 1.042994429254415] d:[-0.00032061661743565395,-0.0006928719431342767]\n",
      "a1 = [-0.00032062 -0.00069287] d1 = [-0.41995375 -0.90754551]\n",
      "a2 = [-0.00032032 -0.00069301] d2 = [ 0.90754551 -0.41995375]\n",
      "b2 = [ 2.93369948e-07 -1.35752762e-07] d2 = [ 0.90754551 -0.41995375]\n",
      "[[-0.41995375 -0.90754551]\n",
      " [ 0.90754551 -0.41995375]]\n",
      "[2.08637687 1.04299449]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def granSchimdt (results, d, dim):\n",
    "    a1 = 0\n",
    "    a2 = 0\n",
    "    b2 = 0\n",
    "\n",
    "    for i in range(dim):\n",
    "        if i == 0:\n",
    "            for j in range(dim):\n",
    "                a1 += results[j] * d[j]\n",
    "                    \n",
    "            d[i] = a1 / LA.norm(a1)\n",
    "\n",
    "            print(f'a1 = {a1} d1 = {d[0]}')\n",
    "        else:\n",
    "            for j in range(1, dim):\n",
    "                a2 += results[j] * d[j]\n",
    "\n",
    "            for j in range(dim-1):\n",
    "                b2 += (np.transpose(a2) @ d[j]) * d[j]\n",
    "            \n",
    "            b2 = a2 - b2\n",
    "\n",
    "            d[i] = b2 / LA.norm(b2)\n",
    "\n",
    "            print(f'a2 = {a2} d2 = {d[1]}')\n",
    "            print(f'b2 = {b2} d2 = {d[1]}')\n",
    "\n",
    "def rosenbrook(function, initialValue, dim):\n",
    "    x1, x2 = initialValue[0], initialValue[1]\n",
    "    nextValue = np.array([\"(x1 + x*d1)\", \"(x2 + x*d2)\"])\n",
    "    nextFunc = function.replace(\"x1\", nextValue[0]).replace(\"x2\", nextValue[1])\n",
    "\n",
    "    d = np.array(\n",
    "            [[1.0,0.0], \n",
    "            [0.0,1.0]]\n",
    "    )\n",
    "\n",
    "    e = 1e-6\n",
    "    k = 0\n",
    "    timeout = 100\n",
    "    time = 0\n",
    "    \n",
    "    while time < timeout:\n",
    "        start = np.array([x1, x2])\n",
    "        end = np.array([0, 0])\n",
    "        results = []\n",
    "\n",
    "        for k in range(dim):          \n",
    "            d1, d2 = d[k][0], d[k][1]\n",
    "\n",
    "            [x, y] = goldenSectionSearch(nextFunc, x1-10 if k == 0 else x2-10, x1+10 if k == 0 else x2+10, x1, x2, d1, d2)\n",
    "            results.append(x)\n",
    "            x1, x2 = eval(nextValue[0]), eval(nextValue[1])\n",
    "\n",
    "            print(f'lmbda{k}: {x} => y{k}: [{x1}, {x2}]')\n",
    "\n",
    "            if k == dim-1:\n",
    "                end = np.array([x1, x2])\n",
    "\n",
    "        resultd = end - start\n",
    "\n",
    "        if LA.norm(resultd) < e:\n",
    "            break\n",
    "        \n",
    "        d1, d2 = resultd[0], resultd[1]\n",
    "\n",
    "        [x, y] = goldenSectionSearch(nextFunc, x1-10 if k == 0 else x2-10, x1+10 if k == 0 else x2+10, x1, x2, d1, d2)\n",
    "\n",
    "        x1, x2 = eval(nextValue[0]), eval(nextValue[1])\n",
    "\n",
    "        print(f'lmbda^{k}: {x} => y3: [{x1}, {x2}] d:[{d1},{d2}]')\n",
    "\n",
    "        granSchimdt(results, d, dim)\n",
    "        print(d)\n",
    "\n",
    "        time += 1\n",
    "\n",
    "    return end\n",
    "    \n",
    "function = \"(x1 - 2)**4 + (x1 - 2*x2)**2\"\n",
    "initialValue = np.array([0, 3])\n",
    "\n",
    "min = rosenbrook(function, initialValue, 2)\n",
    "\n",
    "print(min)\n",
    "\n",
    "plot3D(function, min, 15)\n",
    "plotContour(function, min, 15)"
   ]
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