MPC_New_way&obstacles.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "from casadi import *\n",
    "import scipy.interpolate\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from casadi import *\n",
    "from numpy import *\n",
    "%matplotlib inline\n",
    "#import matplotlib\n",
    "#matplotlib.use('TkAgg')\n",
    "import matplotlib.pyplot as plt\n",
    "class Iteration_Callback(Callback):\n",
    "    def __init__(self, name, nx, ng, opts={}):\n",
    "        Callback.__init__(self)\n",
    "\n",
    "        self.nx = nx\n",
    "        self.ng = ng\n",
    "\n",
    "        self.x_sols = []\n",
    "        self.f_sols = []\n",
    "        self.g_sols = []\n",
    "        self.lam_x_sols = []\n",
    "        self.lam_g_sols = []\n",
    "        self.lam_p_sols = []\n",
    "        '''\n",
    "        #for plotting the graph in realtime\n",
    "        self.fig= plt.figure()\n",
    "        self.ax1= self.fig.add_subplot(1,2,1)\n",
    "        self.ax2= self.fig.add_subplot(2,2,1)\n",
    "        self.fig.show()\n",
    "        '''\n",
    "        # Initialize internal objects\n",
    "        self.construct(name, opts)\n",
    "\n",
    "    def get_n_in(self): return nlpsol_n_out()\n",
    "    def get_n_out(self): return 1\n",
    "    def get_name_in(self, i): return nlpsol_out(i)\n",
    "    def get_name_out(self, i): return \"ret\"\n",
    "\n",
    "    def get_sparsity_in(self, i):\n",
    "        n = nlpsol_out(i)\n",
    "        if n == 'f':\n",
    "            return Sparsity.scalar()\n",
    "        elif n in ('x', 'lam_x'):\n",
    "            return Sparsity.dense(self.nx)\n",
    "        elif n in ('g', 'lam_g'):\n",
    "            return Sparsity.dense(self.ng)\n",
    "        else:\n",
    "            return Sparsity(0, 0)\n",
    "\n",
    "    def eval(self, arg):\n",
    "        \n",
    "        darg = {}\n",
    "        for (i, s) in enumerate(nlpsol_out()): darg[s] = arg[i]\n",
    "        x_sol = [float(elem) for elem in darg['x'].full()]\n",
    "        \n",
    "        self.x_sols.append(x_sol)\n",
    "        self.f_sols.append(darg['f'].full()[0][0])\n",
    "        self.g_sols.append(darg['g'].full()[0][0])\n",
    "        self.lam_x_sols.append(darg['lam_x'].full())\n",
    "        self.lam_g_sols.append(darg['lam_g'].full())\n",
    "        self.lam_p_sols.append(darg['lam_p'].full())\n",
    "        #print(\"solution\",self.f_sols)\n",
    "        '''\n",
    "        #for plotting the graph in realtime\n",
    "        self.ax1.plot(self.x_sols)\n",
    "        self.ax2.plot(self.f_sols)\n",
    "        self.fig.canvas.draw()\n",
    "        self.fig.canvas.flush_events()\n",
    "        plt.show()\n",
    "        '''\n",
    "        plt.plot(self.f_sols,color='C0')\n",
    "        #plt.plot(self.g_sols,color='C0')\n",
    "        #print(self.g_sols)\n",
    "        return[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "[ang_0, ang_1, ang_2, ang_3, ang_4, ang_5, ang_6, ang_7, ang_8, ang_9, ang_10, ang_11, ang_12, ang_13, ang_14, ang_15, ang_16, ang_17, ang_18, ang_19, ang_20, ang_21, ang_22, ang_23, ang_24, ang_25, ang_26, ang_27, ang_28, ang_29, ang_30, ang_31, ang_32, ang_33, ang_34, ang_35, ang_36, ang_37, ang_38, ang_39]\n",
      "func @1=0.00045, @2=(v*t1), @3=1.2, @4=(((v/@3)*tan(ang_0))*t1), @5=(@2+((v*cos(@4))*t1)), @6=(@4+(((v/@3)*tan(ang_1))*t1)), @7=(@5+((v*cos(@6))*t1)), @8=(@6+(((v/@3)*tan(ang_2))*t1)), @9=(@7+((v*cos(@8))*t1)), @10=(@8+(((v/@3)*tan(ang_3))*t1)), @11=(@9+((v*cos(@10))*t1)), @12=(@10+(((v/@3)*tan(ang_4))*t1)), @13=(@11+((v*cos(@12))*t1)), @14=(@12+(((v/@3)*tan(ang_5))*t1)), @15=(@13+((v*cos(@14))*t1)), @16=(@14+(((v/@3)*tan(ang_6))*t1)), @17=(@15+((v*cos(@16))*t1)), @18=(@16+(((v/@3)*tan(ang_7))*t1)), @19=(@17+((v*cos(@18))*t1)), @20=(@18+(((v/@3)*tan(ang_8))*t1)), @21=(@19+((v*cos(@20))*t1)), @22=(@20+(((v/@3)*tan(ang_9))*t1)), @23=(@21+((v*cos(@22))*t1)), @24=(@22+(((v/@3)*tan(ang_10))*t1)), @25=(@23+((v*cos(@24))*t1)), @26=(@24+(((v/@3)*tan(ang_11))*t1)), @27=(@25+((v*cos(@26))*t1)), @28=(@26+(((v/@3)*tan(ang_12))*t1)), @29=(@27+((v*cos(@28))*t1)), @30=(@28+(((v/@3)*tan(ang_13))*t1)), @31=(@29+((v*cos(@30))*t1)), @32=(@30+(((v/@3)*tan(ang_14))*t1)), @33=(@31+((v*cos(@32))*t1)), @34=(@32+(((v/@3)*tan(ang_15))*t1)), @35=(@33+((v*cos(@34))*t1)), @36=(@34+(((v/@3)*tan(ang_16))*t1)), @37=(@35+((v*cos(@36))*t1)), @38=(@36+(((v/@3)*tan(ang_17))*t1)), @39=(@37+((v*cos(@38))*t1)), @40=(@38+(((v/@3)*tan(ang_18))*t1)), @41=(@39+((v*cos(@40))*t1)), @42=2, @43=((v*sin(@4))*t1), @44=(@43+((v*sin(@6))*t1)), @45=(@44+((v*sin(@8))*t1)), @46=(@45+((v*sin(@10))*t1)), @47=(@46+((v*sin(@12))*t1)), @48=(@47+((v*sin(@14))*t1)), @49=(@48+((v*sin(@16))*t1)), @50=(@49+((v*sin(@18))*t1)), @51=(@50+((v*sin(@20))*t1)), @52=(@51+((v*sin(@22))*t1)), @53=(@52+((v*sin(@24))*t1)), @54=(@53+((v*sin(@26))*t1)), @55=(@54+((v*sin(@28))*t1)), @56=(@55+((v*sin(@30))*t1)), @57=(@56+((v*sin(@32))*t1)), @58=(@57+((v*sin(@34))*t1)), @59=(@58+((v*sin(@36))*t1)), @60=(@59+((v*sin(@38))*t1)), @61=(@60+((v*sin(@40))*t1)), @62=-0.0177, @63=1.48, @64=3.39, @65=((((((((((((((((((((((@62*sq(v))+(@63*v))+@64)+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64)), @66=(@40+(((v/@3)*tan(ang_19))*t1)), @67=(@41+((v*cos(@66))*t2)), @68=(@66+(((v/@3)*tan(ang_20))*t2)), @69=(@67+((v*cos(@68))*t2)), @70=(@68+(((v/@3)*tan(ang_21))*t2)), @71=(@69+((v*cos(@70))*t2)), @72=(@70+(((v/@3)*tan(ang_22))*t2)), @73=(@71+((v*cos(@72))*t2)), @74=(@72+(((v/@3)*tan(ang_23))*t2)), @75=(@73+((v*cos(@74))*t2)), @76=(@74+(((v/@3)*tan(ang_24))*t2)), @77=(@75+((v*cos(@76))*t2)), @78=(@76+(((v/@3)*tan(ang_25))*t2)), @79=(@77+((v*cos(@78))*t2)), @80=(@78+(((v/@3)*tan(ang_26))*t2)), @81=(@79+((v*cos(@80))*t2)), @82=(@80+(((v/@3)*tan(ang_27))*t2)), @83=(@81+((v*cos(@82))*t2)), @84=(@82+(((v/@3)*tan(ang_28))*t2)), @85=(@83+((v*cos(@84))*t2)), @86=(@84+(((v/@3)*tan(ang_29))*t2)), @87=(@85+((v*cos(@86))*t2)), @88=(@86+(((v/@3)*tan(ang_30))*t2)), @89=(@87+((v*cos(@88))*t2)), @90=(@88+(((v/@3)*tan(ang_31))*t2)), @91=(@89+((v*cos(@90))*t2)), @92=(@90+(((v/@3)*tan(ang_32))*t2)), @93=(@91+((v*cos(@92))*t2)), @94=(@92+(((v/@3)*tan(ang_33))*t2)), @95=(@93+((v*cos(@94))*t2)), @96=(@94+(((v/@3)*tan(ang_34))*t2)), @97=(@95+((v*cos(@96))*t2)), @98=(@96+(((v/@3)*tan(ang_35))*t2)), @99=(@97+((v*cos(@98))*t2)), @100=(@98+(((v/@3)*tan(ang_36))*t2)), @101=(@99+((v*cos(@100))*t2)), @102=(@100+(((v/@3)*tan(ang_37))*t2)), @103=(@101+((v*cos(@102))*t2)), @104=(@102+(((v/@3)*tan(ang_38))*t2)), @105=(@103+((v*cos(@104))*t2)), @106=(@61+((v*sin(@66))*t2)), @107=(@106+((v*sin(@68))*t2)), @108=(@107+((v*sin(@70))*t2)), @109=(@108+((v*sin(@72))*t2)), @110=(@109+((v*sin(@74))*t2)), @111=(@110+((v*sin(@76))*t2)), @112=(@111+((v*sin(@78))*t2)), @113=(@112+((v*sin(@80))*t2)), @114=(@113+((v*sin(@82))*t2)), @115=(@114+((v*sin(@84))*t2)), @116=(@115+((v*sin(@86))*t2)), @117=(@116+((v*sin(@88))*t2)), @118=(@117+((v*sin(@90))*t2)), @119=(@118+((v*sin(@92))*t2)), @120=(@119+((v*sin(@94))*t2)), @121=(@120+((v*sin(@96))*t2)), @122=(@121+((v*sin(@98))*t2)), @123=(@122+((v*sin(@100))*t2)), @124=(@123+((v*sin(@102))*t2)), @125=(@124+((v*sin(@104))*t2)), @126=3, @127=0.4, @128=1.22, ((((20*sqrt(((@1+sq((@41-@42)))+sq((@61-0.66)))))+@65)+(sqrt(((@1+sq((@105-4)))+sq((@125-@42))))*((((((((((((((((((((((1./sqrt((sq((@2-@126))+1.4884)))+@127)+((1./sqrt((sq((@5-@126))+sq((@43-@128)))))+@127))+((1./sqrt((sq((@7-@126))+sq((@44-@128)))))+@127))+((1./sqrt((sq((@9-@126))+sq((@45-@128)))))+@127))+((1./sqrt((sq((@11-@126))+sq((@46-@128)))))+@127))+((1./sqrt((sq((@13-@126))+sq((@47-@128)))))+@127))+((1./sqrt((sq((@15-@126))+sq((@48-@128)))))+@127))+((1./sqrt((sq((@17-@126))+sq((@49-@128)))))+@127))+((1./sqrt((sq((@19-@126))+sq((@50-@128)))))+@127))+((1./sqrt((sq((@21-@126))+sq((@51-@128)))))+@127))+((1./sqrt((sq((@23-@126))+sq((@52-@128)))))+@127))+((1./sqrt((sq((@25-@126))+sq((@53-@128)))))+@127))+((1./sqrt((sq((@27-@126))+sq((@54-@128)))))+@127))+((1./sqrt((sq((@29-@126))+sq((@55-@128)))))+@127))+((1./sqrt((sq((@31-@126))+sq((@56-@128)))))+@127))+((1./sqrt((sq((@33-@126))+sq((@57-@128)))))+@127))+((1./sqrt((sq((@35-@126))+sq((@58-@128)))))+@127))+((1./sqrt((sq((@37-@126))+sq((@59-@128)))))+@127))+((1./sqrt((sq((@39-@126))+sq((@60-@128)))))+@127))+((1./sqrt((sq((@41-@126))+sq((@61-@128)))))+@127))+(((((((((((((((((((((1./sqrt((sq((@67-@126))+sq((@106-@128)))))+@127)+((1./sqrt((sq((@69-@126))+sq((@107-@128)))))+@127))+((1./sqrt((sq((@71-@126))+sq((@108-@128)))))+@127))+((1./sqrt((sq((@73-@126))+sq((@109-@128)))))+@127))+((1./sqrt((sq((@75-@126))+sq((@110-@128)))))+@127))+((1./sqrt((sq((@77-@126))+sq((@111-@128)))))+@127))+((1./sqrt((sq((@79-@126))+sq((@112-@128)))))+@127))+((1./sqrt((sq((@81-@126))+sq((@113-@128)))))+@127))+((1./sqrt((sq((@83-@126))+sq((@114-@128)))))+@127))+((1./sqrt((sq((@85-@126))+sq((@115-@128)))))+@127))+((1./sqrt((sq((@87-@126))+sq((@116-@128)))))+@127))+((1./sqrt((sq((@89-@126))+sq((@117-@128)))))+@127))+((1./sqrt((sq((@91-@126))+sq((@118-@128)))))+@127))+((1./sqrt((sq((@93-@126))+sq((@119-@128)))))+@127))+((1./sqrt((sq((@95-@126))+sq((@120-@128)))))+@127))+((1./sqrt((sq((@97-@126))+sq((@121-@128)))))+@127))+((1./sqrt((sq((@99-@126))+sq((@122-@128)))))+@127))+((1./sqrt((sq((@101-@126))+sq((@123-@128)))))+@127))+((1./sqrt((sq((@103-@126))+sq((@124-@128)))))+@127))+((1./sqrt((sq((@105-@126))+sq((@125-@128)))))+@127)))))+((((((((((((((((((((@65+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64))+(((@62*sq(v))+(@63*v))+@64)))\n"
     ]
    }
   ],
   "source": [
    "from casadi import *\n",
    "import math\n",
    "#dest = [4,2]\n",
    "#way = [3,1]\n",
    "#obs = [2,0.5]\n",
    "dest = [4,2]\n",
    "way = [2,0.66]\n",
    "obs = [3,1.22]\n",
    "#t=MX.sym('t')\n",
    "#v=MX.sym('v')\n",
    "dt=40\n",
    "dt1=int(dt/2)\n",
    "print(dt1)\n",
    "rob_rad=0.2\n",
    "obs_rad=0.2\n",
    "ang=SX.sym(\"ang\",dt)\n",
    "v=SX.sym(\"v\")\n",
    "t1=SX.sym(\"t1\")\n",
    "t2=SX.sym(\"t2\")\n",
    "print(ang)\n",
    "func,func1,func2=0,0,0\n",
    "g1=0\n",
    "g2=0\n",
    "g=0\n",
    "fuel_cost=0\n",
    "penalty=20\n",
    "obstacle=0\n",
    "theta=0\n",
    "coff=0.00045\n",
    "for i in range(0,dt1):\n",
    "    func1=func1+v*cos(theta)*t1\n",
    "    func2=func2+v*sin(theta)*t1\n",
    "    theta=theta + (v / 1.2)*tan(ang[i])*t1\n",
    "    g1+=(1/(sqrt((func1-obs[0])**2+(func2-obs[1])**2))+(rob_rad+obs_rad))\n",
    "    fuel_cost+=(-0.0177*v**2+1.48*v+3.39)\n",
    "    if i==dt1-1:\n",
    "            #g1=(sqrt((func1-obs[0])**2+(func2-obs[1])**2))+(rob_rad+obs_rad)\n",
    "            func_con=(sqrt(coff+(func1-way[0])**2+(func2-way[1])**2))*penalty+fuel_cost\n",
    "for i in range(dt1,dt):\n",
    "    func1=func1+v*cos(theta)*t2\n",
    "    func2=func2+v*sin(theta)*t2\n",
    "    theta=theta + (v / 1.2)*tan(ang[i])*t2\n",
    "    g2+=(1/(sqrt((func1-obs[0])**2+(func2-obs[1])**2)))+(rob_rad+obs_rad)\n",
    "    fuel_cost+=(-0.0177*v**2+1.48*v+3.39)\n",
    "    if i==dt-1:\n",
    "        #g2=(sqrt((func1-obs[0])**2+(func2-obs[1])**2))+(rob_rad+obs_rad)\n",
    "        func=func_con+(sqrt(coff+(func1-dest[0])**2+(func2-dest[1])**2))*(g1+g2)+fuel_cost\n",
    "        print(\"func\",func)\n",
    "            #print(\"g\",g)\n",
    "\n",
    "            \n",
    "nlp= {}\n",
    "nlp['x']=vertcat(ang,v,t1,t2)\n",
    "#nlp['x']=vertcat(v,t) \n",
    "nlp['f']=func\n",
    "nlp['g']=g\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F:(x0[43],p[],lbx[43],ubx[43],lbg,ubg,lam_x0[43],lam_g0)->(x[43],f,g,lam_x[43],lam_g,lam_p[]) IpoptInterface\n",
      "0.45\n",
      "[0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.2, 0.2, 0.2]\n",
      "This is Ipopt version 3.12.3, running with linear solver mumps.\n",
      "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
      "\n",
      "Number of nonzeros in equality constraint Jacobian...:        0\n",
      "Number of nonzeros in inequality constraint Jacobian.:        0\n",
      "Number of nonzeros in Lagrangian Hessian.............:      903\n",
      "\n",
      "Total number of variables............................:       43\n",
      "                     variables with only lower bounds:        0\n",
      "                variables with lower and upper bounds:       43\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:        1\n",
      "        inequality constraints with only lower bounds:        0\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        0\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0 4.1982861e+002 0.00e+000 1.35e+003  -1.0 0.00e+000    -  0.00e+000 0.00e+000   0\n",
      "   1 3.1671614e+002 0.00e+000 5.83e+002  -1.0 1.35e-001   4.0 9.71e-001 1.00e+000f  1\n",
      "   2 3.0288975e+002 0.00e+000 8.98e+001  -1.0 6.97e-002    -  2.75e-001 5.00e-001f  2\n",
      "   3 3.0261173e+002 0.00e+000 7.41e+002  -1.0 6.71e-001    -  7.83e-002 1.00e+000f  1\n",
      "   4 2.9450756e+002 0.00e+000 8.53e+002  -1.0 8.41e-001    -  3.85e-001 5.00e-001f  2\n",
      "   5 2.9048073e+002 0.00e+000 3.40e+001  -1.0 7.07e-003   3.5 1.00e+000 1.00e+000f  1\n",
      "   6 2.5325200e+002 0.00e+000 4.86e+003  -1.0 3.13e+000    -  2.62e-002 1.55e-001f  1\n",
      "   7 2.4702551e+002 0.00e+000 4.40e+003  -1.0 8.86e-002   3.0 1.00e+000 5.51e-002f  1\n",
      "   8 2.2911654e+002 0.00e+000 6.22e+003  -1.0 1.96e-001   2.6 1.00e+000 2.26e-001f  1\n",
      "   9 2.2690813e+002 0.00e+000 7.31e+003  -1.0 2.84e-004   7.5 1.00e+000 1.00e+000f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  10 2.2378038e+002 0.00e+000 3.27e+003  -1.0 5.19e-004   7.0 1.00e+000 1.00e+000f  1\n",
      "  11 2.2323614e+002 0.00e+000 5.92e+002  -1.0 2.49e-004   6.6 1.00e+000 1.00e+000f  1\n",
      "  12 2.2321568e+002 0.00e+000 1.31e+002  -1.0 1.10e-004   6.1 1.00e+000 1.00e+000f  1\n",
      "  13 2.2319136e+002 0.00e+000 8.06e+001  -1.0 2.02e-004   5.6 1.00e+000 1.00e+000f  1\n",
      "  14 2.2309898e+002 0.00e+000 4.35e+001  -1.0 3.27e-004   5.1 1.00e+000 1.00e+000f  1\n",
      "  15 2.2277174e+002 0.00e+000 3.33e+001  -1.0 7.50e-004   4.6 1.00e+000 1.00e+000f  1\n",
      "  16 2.2158193e+002 0.00e+000 1.24e+002  -1.0 2.46e-003   4.2 1.00e+000 1.00e+000f  1\n",
      "  17 2.1636322e+002 0.00e+000 1.45e+003  -1.0 1.03e-002   3.7 1.00e+000 1.00e+000f  1\n",
      "  18 2.1566991e+002 0.00e+000 1.41e+003  -1.0 5.21e-003   3.2 1.00e+000 1.00e+000f  1\n",
      "  19 2.1514897e+002 0.00e+000 5.41e+001  -1.0 2.24e-002   2.7 1.00e+000 1.00e+000f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  20 2.1656109e+002 0.00e+000 1.23e+003  -1.0 2.51e-001    -  6.90e-001 1.00e+000f  1\n",
      "  21 2.1585065e+002 0.00e+000 2.67e+003  -1.0 3.99e-002   2.3 1.00e+000 5.00e-001f  2\n",
      "  22 2.1384113e+002 0.00e+000 2.50e+003  -1.0 8.94e-001   1.8 3.56e-001 2.65e-002f  2\n",
      "  23 2.1250846e+002 0.00e+000 1.19e+003  -1.0 1.26e-002   2.2 1.00e+000 5.00e-001f  2\n",
      "  24 2.1232950e+002 0.00e+000 7.05e+001  -1.0 2.43e-002   1.7 1.00e+000 1.00e+000f  1\n",
      "  25 2.1223373e+002 0.00e+000 2.37e+003  -1.0 5.58e+000    -  1.89e-001 1.55e-002f  5\n",
      "  26 2.1143545e+002 0.00e+000 1.14e+003  -1.0 1.56e-001    -  1.00e+000 1.00e+000f  1\n",
      "  27 2.1099320e+002 0.00e+000 1.80e+003  -1.0 1.60e-001   1.3 1.00e+000 5.00e-001f  2\n",
      "  28 2.1096339e+002 0.00e+000 6.86e+002  -1.0 6.49e-002    -  1.00e+000 1.00e+000f  1\n",
      "  29 2.1093987e+002 0.00e+000 1.73e+003  -1.0 1.39e-001    -  1.00e+000 5.00e-001f  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  30 2.1086066e+002 0.00e+000 9.36e+002  -1.0 6.25e-002    -  1.00e+000 1.00e+000f  1\n",
      "  31 2.1082476e+002 0.00e+000 3.16e+003  -1.0 1.08e-001    -  1.00e+000 1.00e+000f  1\n",
      "  32 2.1060433e+002 0.00e+000 2.36e+002  -1.0 3.69e-002    -  1.00e+000 1.00e+000f  1\n",
      "  33 2.1052941e+002 0.00e+000 1.52e+003  -1.0 1.53e-001   0.8 1.00e+000 5.00e-001f  2\n",
      "  34 2.1044655e+002 0.00e+000 1.38e+003  -1.0 7.56e-002    -  1.00e+000 1.00e+000f  1\n",
      "  35 2.1035322e+002 0.00e+000 1.71e+003  -1.0 8.36e-002    -  1.00e+000 1.00e+000f  1\n",
      "  36 2.1024943e+002 0.00e+000 1.12e+003  -1.0 7.04e-002    -  1.00e+000 1.00e+000f  1\n",
      "  37 2.1018944e+002 0.00e+000 2.66e+003  -1.0 1.06e-001    -  1.00e+000 1.00e+000f  1\n",
      "  38 2.1005699e+002 0.00e+000 2.96e+002  -1.0 4.94e-002    -  1.00e+000 1.00e+000f  1\n",
      "  39 2.1000470e+002 0.00e+000 2.00e+003  -1.0 3.49e-001    -  1.00e+000 2.50e-001f  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  40 2.0991564e+002 0.00e+000 8.78e+002  -1.0 6.71e-002    -  1.00e+000 1.00e+000f  1\n",
      "  41 2.0986467e+002 0.00e+000 1.58e+003  -1.0 1.45e-001    -  1.00e+000 5.00e-001f  2\n",
      "  42 2.0978572e+002 0.00e+000 1.51e+003  -1.0 8.58e-002    -  1.00e+000 1.00e+000f  1\n",
      "  43 2.0971200e+002 0.00e+000 1.70e+003  -1.0 9.17e-002    -  1.00e+000 1.00e+000f  1\n",
      "  44 2.0963538e+002 0.00e+000 1.36e+003  -1.0 8.39e-002    -  1.00e+000 1.00e+000f  1\n",
      "  45 2.0957379e+002 0.00e+000 2.16e+003  -1.0 1.05e-001    -  1.00e+000 1.00e+000f  1\n",
      "  46 2.0949284e+002 0.00e+000 8.20e+002  -1.0 6.98e-002    -  1.00e+000 1.00e+000f  1\n",
      "  47 2.0945043e+002 0.00e+000 1.83e+003  -1.0 1.72e-001    -  1.00e+000 5.00e-001f  2\n",
      "  48 2.0938210e+002 0.00e+000 1.23e+003  -1.0 8.40e-002    -  1.00e+000 1.00e+000f  1\n",
      "  49 2.0933677e+002 0.00e+000 2.72e+003  -1.0 1.23e-001    -  1.00e+000 1.00e+000f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  50 2.0925441e+002 0.00e+000 4.61e+002  -1.0 6.02e-002    -  1.00e+000 1.00e+000f  1\n",
      "  51 2.0925478e+002 0.00e+000 4.12e+003  -1.0 3.00e-001    -  1.00e+000 5.00e-001f  2\n",
      "  52 2.0913055e+002 0.00e+000 1.68e+002  -1.0 4.27e-002    -  1.00e+000 1.00e+000f  1\n",
      "  53 2.0911954e+002 0.00e+000 3.50e+003  -1.0 1.43e-001   0.3 1.00e+000 1.00e+000f  1\n",
      "  54 2.0903574e+002 0.00e+000 1.31e+002  -1.0 5.10e-002    -  1.00e+000 1.00e+000f  1\n",
      "  55 2.0900056e+002 0.00e+000 1.50e+003  -1.0 7.34e-001    -  1.00e+000 1.25e-001f  4\n",
      "  56 2.0896153e+002 0.00e+000 1.79e+003  -1.0 1.08e-001    -  1.00e+000 1.00e+000f  1\n",
      "  57 2.0891220e+002 0.00e+000 1.23e+003  -1.0 9.19e-002    -  1.00e+000 1.00e+000f  1\n",
      "  58 2.0887364e+002 0.00e+000 2.44e+003  -1.0 1.28e-001    -  1.00e+000 1.00e+000f  1\n",
      "  59 2.0882164e+002 0.00e+000 5.53e+002  -1.0 6.71e-002    -  1.00e+000 1.00e+000f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  60 2.0879318e+002 0.00e+000 2.39e+003  -1.0 2.40e-001    -  1.00e+000 5.00e-001f  2\n",
      "  61 2.0874850e+002 0.00e+000 4.80e+002  -1.0 6.38e-002    -  1.00e+000 1.00e+000f  1\n",
      "  62 2.0872036e+002 0.00e+000 2.19e+003  -1.0 2.35e-001    -  1.00e+000 5.00e-001f  2\n",
      "  63 2.0868377e+002 0.00e+000 4.06e+002  -1.0 5.93e-002    -  1.00e+000 1.00e+000f  1\n",
      "  64 2.0865684e+002 0.00e+000 1.55e+003  -1.0 1.98e-001    -  1.00e+000 5.00e-001f  2\n",
      "  65 2.0863150e+002 0.00e+000 4.00e+002  -1.0 5.73e-002    -  1.00e+000 1.00e+000f  1\n",
      "  66 2.0860314e+002 0.00e+000 1.62e+003  -1.0 1.11e-001    -  1.00e+000 1.00e+000f  1\n",
      "  67 2.0859021e+002 0.00e+000 1.30e+001  -1.0 1.61e-002    -  1.00e+000 1.00e+000f  1\n",
      "  68 2.0858223e+002 0.00e+000 1.26e+002  -1.0 3.13e-002    -  1.00e+000 1.00e+000f  1\n",
      "  69 2.0858282e+002 0.00e+000 4.53e-001  -1.0 1.71e-003    -  1.00e+000 1.00e+000f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  70 2.0785542e+002 0.00e+000 6.27e+003  -1.7 1.76e-001    -  1.00e+000 1.00e+000f  1\n",
      "  71 2.0759609e+002 0.00e+000 4.01e+003  -1.7 3.11e-001  -0.2 7.17e-001 4.67e-001f  1\n",
      "  72 2.0750104e+002 0.00e+000 7.25e+002  -1.7 4.92e-002    -  1.00e+000 1.00e+000f  1\n",
      "  73 2.0748939e+002 0.00e+000 3.91e+001  -1.7 3.59e-002    -  1.00e+000 1.00e+000f  1\n",
      "  74 2.0749235e+002 0.00e+000 7.41e+000  -1.7 8.10e-003    -  1.00e+000 1.00e+000f  1\n",
      "  75 2.0749254e+002 0.00e+000 3.95e-002  -1.7 5.63e-004    -  1.00e+000 1.00e+000f  1\n",
      "  76 2.0740605e+002 0.00e+000 5.41e+002  -2.5 5.50e-002    -  1.00e+000 1.00e+000f  1\n",
      "  77 2.0735296e+002 0.00e+000 6.62e+001  -2.5 6.60e-002    -  1.00e+000 1.00e+000f  1\n",
      "  78 2.0735102e+002 0.00e+000 5.69e+000  -2.5 1.43e-002    -  1.00e+000 1.00e+000f  1\n",
      "  79 2.0735109e+002 0.00e+000 1.06e+001  -2.5 1.22e-002    -  1.00e+000 1.00e+000f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  80 2.0735107e+002 0.00e+000 2.02e-002  -2.5 5.86e-004    -  1.00e+000 1.00e+000f  1\n",
      "  81 2.0731404e+002 0.00e+000 2.89e+001  -3.8 5.27e-002    -  8.71e-001 1.00e+000f  1\n",
      "  82 2.0730447e+002 0.00e+000 1.08e+001  -3.8 3.27e-002    -  1.00e+000 1.00e+000f  1\n",
      "  83 2.0730404e+002 0.00e+000 7.87e-001  -3.8 3.73e-003    -  1.00e+000 1.00e+000f  1\n",
      "  84 2.0730404e+002 0.00e+000 6.65e+000  -3.8 9.45e-003    -  1.00e+000 1.00e+000f  1\n",
      "  85 2.0730404e+002 0.00e+000 3.74e-002  -3.8 5.03e-004    -  1.00e+000 1.00e+000f  1\n",
      "  86 2.0730404e+002 0.00e+000 5.08e-002  -3.8 5.08e-004    -  1.00e+000 1.00e+000f  1\n",
      "  87 2.0730404e+002 0.00e+000 3.43e-006  -3.8 4.67e-006    -  1.00e+000 1.00e+000f  1\n",
      "  88 2.0730087e+002 0.00e+000 2.06e+001  -5.7 1.09e-002    -  8.60e-001 1.00e+000f  1\n",
      "  89 2.0730081e+002 0.00e+000 1.69e+000  -5.7 3.34e-003    -  1.00e+000 1.00e+000f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  90 2.0730081e+002 0.00e+000 2.04e+001  -5.7 4.34e-002    -  1.00e+000 2.50e-001f  3\n",
      "  91 2.0730079e+002 0.00e+000 2.23e+000  -5.7 3.86e-003    -  1.00e+000 1.00e+000f  1\n",
      "  92 2.0730079e+002 0.00e+000 1.28e+001  -5.7 3.45e-002    -  1.00e+000 2.50e-001f  3\n",
      "  93 2.0730078e+002 0.00e+000 5.57e+000  -5.7 6.26e-003    -  1.00e+000 1.00e+000f  1\n",
      "  94 2.0730077e+002 0.00e+000 1.01e+001  -5.7 1.45e-002    -  1.00e+000 5.00e-001f  2\n",
      "  95 2.0730076e+002 0.00e+000 8.95e+000  -5.7 8.19e-003    -  1.00e+000 1.00e+000f  1\n",
      "  96 2.0730075e+002 0.00e+000 1.12e+001  -5.7 9.37e-003    -  1.00e+000 1.00e+000f  1\n",
      "  97 2.0730074e+002 0.00e+000 7.11e+000  -5.7 7.63e-003    -  1.00e+000 1.00e+000f  1\n",
      "  98 2.0730073e+002 0.00e+000 1.75e+001  -5.7 1.22e-002    -  1.00e+000 1.00e+000f  1\n",
      "  99 2.0730072e+002 0.00e+000 2.90e+000  -5.7 5.14e-003    -  1.00e+000 1.00e+000f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 100 2.0730072e+002 0.00e+000 8.46e+000  -5.7 3.07e-002    -  1.00e+000 2.50e-001f  3\n",
      " 101 2.0730071e+002 0.00e+000 1.21e+001  -5.7 1.09e-002    -  1.00e+000 1.00e+000f  1\n",
      " 102 2.0730070e+002 0.00e+000 5.91e+000  -5.7 7.85e-003    -  1.00e+000 1.00e+000f  1\n",
      " 103 2.0730069e+002 0.00e+000 2.37e+001  -5.7 1.62e-002    -  1.00e+000 1.00e+000f  1\n",
      " 104 2.0730068e+002 0.00e+000 1.48e+000  -5.7 4.26e-003    -  1.00e+000 1.00e+000f  1\n",
      " 105 2.0730067e+002 0.00e+000 2.03e+001  -5.7 6.31e-002    -  1.00e+000 2.50e-001f  3\n",
      " 106 2.0730066e+002 0.00e+000 1.91e+000  -5.7 5.17e-003    -  1.00e+000 1.00e+000f  1\n",
      " 107 2.0730065e+002 0.00e+000 1.23e+001  -5.7 5.11e-002    -  1.00e+000 2.50e-001f  3\n",
      " 108 2.0730064e+002 0.00e+000 4.75e+000  -5.7 8.72e-003    -  1.00e+000 1.00e+000f  1\n",
      " 109 2.0730063e+002 0.00e+000 9.49e+000  -5.7 2.22e-002    -  1.00e+000 5.00e-001f  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 110 2.0730062e+002 0.00e+000 7.11e+000  -5.7 1.15e-002    -  1.00e+000 1.00e+000f  1\n",
      " 111 2.0730061e+002 0.00e+000 1.15e+001  -5.7 1.53e-002    -  1.00e+000 1.00e+000f  1\n",
      " 112 2.0730060e+002 0.00e+000 4.18e+000  -5.7 9.74e-003    -  1.00e+000 1.00e+000f  1\n",
      " 113 2.0730060e+002 0.00e+000 8.88e+000  -5.7 2.55e-002    -  1.00e+000 5.00e-001f  2\n",
      " 114 2.0730059e+002 0.00e+000 6.11e+000  -5.7 1.27e-002    -  1.00e+000 1.00e+000f  1\n",
      " 115 2.0730058e+002 0.00e+000 1.16e+001  -5.7 1.81e-002    -  1.00e+000 1.00e+000f  1\n",
      " 116 2.0730057e+002 0.00e+000 3.14e+000  -5.7 1.00e-002    -  1.00e+000 1.00e+000f  1\n",
      " 117 2.0730057e+002 0.00e+000 1.03e+001  -5.7 3.35e-002    -  1.00e+000 5.00e-001f  2\n",
      " 118 2.0730056e+002 0.00e+000 3.46e+000  -5.7 1.15e-002    -  1.00e+000 1.00e+000f  1\n",
      " 119 2.0730055e+002 0.00e+000 7.64e+000  -5.7 3.00e-002    -  1.00e+000 5.00e-001f  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 120 2.0730055e+002 0.00e+000 4.78e+000  -5.7 1.45e-002    -  1.00e+000 1.00e+000f  1\n",
      " 121 2.0730054e+002 0.00e+000 9.05e+000  -5.7 1.97e-002    -  1.00e+000 1.00e+000f  1\n",
      " 122 2.0730053e+002 0.00e+000 1.74e+000  -5.7 7.91e-003    -  1.00e+000 1.00e+000f  1\n",
      " 123 2.0730053e+002 0.00e+000 5.56e+000  -5.7 1.64e-002    -  1.00e+000 5.00e-001f  2\n",
      " 124 2.0730053e+002 0.00e+000 1.82e+000  -5.7 5.46e-003    -  1.00e+000 1.00e+000f  1\n",
      " 125 2.0730053e+002 0.00e+000 4.04e+000  -5.7 1.54e-002    -  1.00e+000 5.00e-001f  2\n",
      " 126 2.0730053e+002 0.00e+000 2.54e+000  -5.7 7.29e-003    -  1.00e+000 1.00e+000f  1\n",
      " 127 2.0730053e+002 0.00e+000 5.47e+000  -5.7 1.06e-002    -  1.00e+000 1.00e+000f  1\n",
      " 128 2.0730053e+002 0.00e+000 1.11e+000  -5.7 4.95e-003    -  1.00e+000 1.00e+000f  1\n",
      " 129 2.0730052e+002 0.00e+000 5.64e+000  -5.7 1.78e-002    -  1.00e+000 5.00e-001f  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 130 2.0730052e+002 0.00e+000 8.75e-001  -5.7 4.12e-003    -  1.00e+000 1.00e+000f  1\n",
      " 131 2.0730052e+002 0.00e+000 6.87e+000  -5.7 1.80e-002    -  1.00e+000 5.00e-001f  2\n",
      " 132 2.0730052e+002 0.00e+000 4.89e-001  -5.7 2.67e-003    -  1.00e+000 1.00e+000f  1\n",
      " 133 2.0730052e+002 0.00e+000 3.96e+000  -5.7 2.74e-002    -  1.00e+000 2.50e-001f  3\n",
      " 134 2.0730052e+002 0.00e+000 1.22e+000  -5.7 4.19e-003    -  1.00e+000 1.00e+000f  1\n",
      " 135 2.0730052e+002 0.00e+000 3.06e+000  -5.7 1.16e-002    -  1.00e+000 5.00e-001f  2\n",
      " 136 2.0730052e+002 0.00e+000 1.59e+000  -5.7 4.81e-003    -  1.00e+000 1.00e+000f  1\n",
      " 137 2.0730052e+002 0.00e+000 4.81e+000  -5.7 8.21e-003    -  1.00e+000 1.00e+000f  1\n",
      " 138 2.0730051e+002 0.00e+000 4.82e-001  -5.7 2.67e-003    -  1.00e+000 1.00e+000f  1\n",
      " 139 2.0730051e+002 0.00e+000 1.85e+000  -5.7 1.74e-002    -  1.00e+000 2.50e-001f  3\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 140 2.0730051e+002 0.00e+000 2.04e+000  -5.7 5.40e-003    -  1.00e+000 1.00e+000f  1\n",
      " 141 2.0730051e+002 0.00e+000 1.33e+000  -5.7 4.29e-003    -  1.00e+000 1.00e+000f  1\n",
      " 142 2.0730051e+002 0.00e+000 1.98e+000  -5.7 5.07e-003    -  1.00e+000 1.00e+000f  1\n",
      " 143 2.0730051e+002 0.00e+000 5.95e-001  -5.7 2.73e-003    -  1.00e+000 1.00e+000f  1\n",
      " 144 2.0730051e+002 0.00e+000 2.71e+000  -5.7 6.11e-003    -  1.00e+000 1.00e+000f  1\n",
      " 145 2.0730051e+002 0.00e+000 8.58e-002  -5.7 1.14e-003    -  1.00e+000 1.00e+000f  1\n",
      " 146 2.0730051e+002 0.00e+000 6.91e-001  -5.7 1.60e-002    -  1.00e+000 2.50e-001f  3\n",
      " 147 2.0730051e+002 0.00e+000 4.45e-001  -5.7 3.16e-003    -  1.00e+000 1.00e+000f  1\n",
      " 148 2.0730051e+002 0.00e+000 6.02e-001  -5.7 4.05e-003    -  1.00e+000 1.00e+000f  1\n",
      " 149 2.0730051e+002 0.00e+000 1.92e-001  -5.7 2.48e-003    -  1.00e+000 1.00e+000f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 150 2.0730051e+002 0.00e+000 5.45e-001  -5.7 4.46e-003    -  1.00e+000 1.00e+000f  1\n",
      " 151 2.0730051e+002 0.00e+000 3.65e-002  -5.7 1.23e-003    -  1.00e+000 1.00e+000f  1\n",
      " 152 2.0730051e+002 0.00e+000 2.41e-001  -5.7 3.26e-003    -  1.00e+000 1.00e+000f  1\n",
      " 153 2.0730051e+002 0.00e+000 1.45e-003  -5.7 2.78e-004    -  1.00e+000 1.00e+000f  1\n",
      " 154 2.0730051e+002 0.00e+000 2.43e-003  -5.7 3.50e-004    -  1.00e+000 1.00e+000f  1\n",
      " 155 2.0730051e+002 0.00e+000 9.37e-008  -5.7 2.40e-006    -  1.00e+000 1.00e+000f  1\n",
      " 156 2.0730049e+002 0.00e+000 1.35e+001  -8.6 7.31e-002    -  8.09e-001 2.47e-001f  3\n",
      " 157 2.0730046e+002 0.00e+000 3.81e+000  -8.6 1.45e-002    -  9.76e-001 8.38e-001f  1\n",
      " 158 2.0730045e+002 0.00e+000 1.25e+001  -8.6 2.54e-002    -  9.16e-001 8.53e-001f  1\n",
      " 159 2.0730044e+002 0.00e+000 6.18e+000  -8.6 7.57e-003    -  1.00e+000 5.40e-001f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 160 2.0730044e+002 0.00e+000 4.48e+000  -8.6 1.20e-002    -  1.00e+000 3.81e-001f  1\n",
      " 161 2.0730044e+002 0.00e+000 3.79e+000  -8.6 1.36e-002    -  1.00e+000 3.55e-001f  1\n",
      " 162 2.0730044e+002 0.00e+000 3.26e+000  -8.6 1.44e-002    -  1.00e+000 1.99e-001f  1\n",
      " 163 2.0730044e+002 0.00e+000 2.79e+000  -8.6 1.21e-002    -  1.00e+000 2.76e-001f  1\n",
      " 164 2.0730044e+002 0.00e+000 5.55e+000  -8.6 1.06e-002    -  1.00e+000 1.00e+000f  1\n",
      " 165 2.0730043e+002 0.00e+000 1.74e+000  -8.6 6.64e-003    -  1.00e+000 1.00e+000f  1\n",
      " 166 2.0730043e+002 0.00e+000 1.69e+000  -8.6 2.14e-002    -  1.00e+000 6.60e-002f  1\n",
      " 167 2.0730043e+002 0.00e+000 2.67e+000  -8.6 2.20e-002    -  1.00e+000 5.00e-001f  2\n",
      " 168 2.0730043e+002 0.00e+000 2.32e+000  -8.6 1.28e-002    -  1.00e+000 1.00e+000f  1\n",
      " 169 2.0730043e+002 0.00e+000 1.55e+000  -8.6 6.67e-003    -  1.00e+000 3.71e-001f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 170 2.0730043e+002 0.00e+000 3.35e-001  -8.6 1.19e-003    -  1.00e+000 7.91e-001f  1\n",
      " 171 2.0730043e+002 0.00e+000 5.52e-003  -8.6 7.68e-004    -  1.00e+000 1.00e+000f  1\n",
      " 172 2.0730043e+002 0.00e+000 1.37e-004  -8.6 9.33e-005    -  1.00e+000 1.00e+000f  1\n",
      " 173 2.0730043e+002 0.00e+000 1.35e-008  -8.6 9.87e-007    -  1.00e+000 1.00e+000f  1\n",
      " 174 2.0730043e+002 0.00e+000 1.72e-004  -9.0 6.69e-005    -  1.00e+000 1.00e+000f  1\n",
      " 175 2.0730043e+002 0.00e+000 1.82e-010  -9.0 1.57e-008    -  1.00e+000 1.00e+000f  1\n",
      "\n",
      "Number of Iterations....: 175\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:  1.8940003482788336e+002   2.0730043287118528e+002\n",
      "Dual infeasibility......:  1.8217063576846846e-010   1.9938777564397224e-010\n",
      "Constraint violation....:  0.0000000000000000e+000   0.0000000000000000e+000\n",
      "Complementarity.........:  9.0909098180051394e-010   9.9501013406779551e-010\n",
      "Overall NLP error.......:  9.0909098180051394e-010   9.9501013406779551e-010\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 376\n",
      "Number of objective gradient evaluations             = 176\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 376\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 176\n",
      "Number of Lagrangian Hessian evaluations             = 175\n",
      "Total CPU secs in IPOPT (w/o function evaluations)   =      0.272\n",
      "Total CPU secs in NLP function evaluations           =      0.039\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "           F  :   t_proc      (avg)   t_wall      (avg)    n_eval\n",
      "callback_fun  | 167.00ms (948.86us) 166.54ms (946.26us)       176\n",
      "       nlp_f  |   4.00ms ( 10.64us)   3.90ms ( 10.37us)       376\n",
      "       nlp_g  |        0 (       0)        0 (       0)       376\n",
      "  nlp_grad_f  |   2.00ms ( 11.30us)   1.96ms ( 11.05us)       177\n",
      "  nlp_hess_l  |  31.00ms (177.14us)  30.76ms (175.80us)       175\n",
      "   nlp_jac_g  |        0 (       0)        0 (       0)       177\n",
      "       total  | 311.00ms (311.00ms) 310.77ms (310.77ms)         1\n",
      "solved output [DM([8.70807e-006, 0.0646742, 0.449992, 0.449996, 0.449997, 0.449997, 0.449997, 0.449997, 0.449997, 0.449997, 0.449997, 0.449996, 0.449994, 0.449991, 0.449979, 0.217772, 3.33968e-005, 1.85655e-005, 1.42433e-005, 1.30128e-005, 3.32342e-008, 1.16676e-008, 4.46304e-009, 8.59788e-010, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.01483e-009, 6.00982e-009, 1.40033e-008, 3.79884e-008, 0.225, 0.02, 5.38846, 6.01983])]\n",
      "solved function output 207.3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.99852 0.661441\n",
      "3.99982 2.00043\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#solver=F(x0=[0.1,2],lbx=lbx,ubx=ubx)\n",
    "#fcn = Function('f', nlp['x'], [f])\n",
    "mycallback = Iteration_Callback('mycallback',(dt+3), 1)\n",
    "opts = {}\n",
    "opts['iteration_callback'] = mycallback\n",
    "opts['ipopt.tol'] = 1e-8\n",
    "#opts['ipopt.bound_frac'] = 0.01\n",
    "#opts['ipopt.bound_push'] = 0.01\n",
    "#opts['ipopt.hessian_approximation']= \"limited-memory\"\n",
    "opts[\"ipopt.max_iter\"]=5000\n",
    "#create solver instance\n",
    "F = nlpsol('F','ipopt',nlp,opts);\n",
    "#F=qpsol('F','qpoases',nlp);\n",
    "print(F)\n",
    "\n",
    "#ang_upl=[0.4,3.14,6]\n",
    "ang_upl=[0.45]\n",
    "for j in range(0,len(ang_upl)):\n",
    "    x0=[]\n",
    "    lbx=[]\n",
    "    ubx=[]\n",
    "# the loop for setting angle\n",
    "    for i in range(0,dt):\n",
    "        x0.append(0.002)\n",
    "        lbx.append(0.00)\n",
    "        #ubx.append(0.5)  #previous value\n",
    "        ang_up=ang_upl[j]\n",
    "        ubx.append(ang_up)\n",
    "# the loop for setting velocity  \n",
    "    for i in range(0,1):\n",
    "        x0.append(0.2)\n",
    "        lbx.append(0.02)\n",
    "        ubx.append(2)\n",
    "    # the loop for setting sample time \n",
    "    for i in range(0,1):\n",
    "        x0.append(0.2)\n",
    "        lbx.append(1)\n",
    "        ubx.append(100)\n",
    "    for i in range(0,1):\n",
    "        x0.append(0.2)\n",
    "        lbx.append(1)\n",
    "        ubx.append(100)\n",
    "    print(ang_up)\n",
    "    print(x0)\n",
    "    solver=F(x0=x0,lbx=lbx,ubx=ubx)\n",
    "    out=[]\n",
    "    out.append(solver[\"x\"])\n",
    "    print(\"solved output\",out)\n",
    "    print(\"solved function output\",solver[\"f\"])\n",
    "    plt.legend([\"Cost\"])\n",
    "    plt.xlabel(\"Iterations\")\n",
    "    plt.ylabel(\"Cost Value\")\n",
    "    plt.show()\n",
    "    ang=[]\n",
    "    v=[]\n",
    "    t1=[]\n",
    "    t2=[]\n",
    "    for i in range(0,dt):\n",
    "        ang.append(out[0][i])\n",
    "    v.append(out[0][dt])\n",
    "    t1.append(out[0][dt+1])\n",
    "    t2.append(out[0][dt+2])\n",
    "    xc,yc=[],[]\n",
    "    theta=0.0\n",
    "    x1,x2=0,0\n",
    "    for i in range(0,dt1):\n",
    "        x1=x1+v[0]*math.cos(theta)*t1[0]\n",
    "        x2=x2+v[0]*math.sin(theta)*t1[0]\n",
    "        theta = theta + (v[0] / 1.2) * math.tan(ang[i])*t1[0]\n",
    "        xc.append(x1)\n",
    "        yc.append(x2)\n",
    "    print(x1,x2)\n",
    "    for i in range(dt1,dt):\n",
    "        x1=x1+v[0]*math.cos(theta)*t2[0]\n",
    "        x2=x2+v[0]*math.sin(theta)*t2[0]\n",
    "        theta = theta + (v[0] / 1.2) * math.tan(ang[i])*t2[0]\n",
    "        xc.append(x1)\n",
    "        yc.append(x2)\n",
    "    print(x1,x2)\n",
    "    plt.plot(xc,yc,dest[0],dest[1],\"o\",way[0],way[1],\"o\",obs[0],obs[1],\"o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.99852 0.661441\n",
      "[DM(0.107769), DM(0.215538), DM(0.323306), DM(0.430945), DM(0.538252), DM(0.645027), DM(0.751067), DM(0.856175), DM(0.960151), DM(1.0628), DM(1.16393), DM(1.26335), DM(1.36087), DM(1.45631), DM(1.54949), DM(1.64023), DM(1.7298), DM(1.81937), DM(1.90894), DM(1.99852), DM(2.09858), DM(2.19865), DM(2.29871), DM(2.39878), DM(2.49884), DM(2.59891), DM(2.69897), DM(2.79904), DM(2.8991), DM(2.99917), DM(3.09924), DM(3.1993), DM(3.29937), DM(3.39943), DM(3.4995), DM(3.59956), DM(3.69963), DM(3.79969), DM(3.89976), DM(3.99982)]\n",
      "[DM(0), DM(8.4281e-008), DM(0.000626989), DM(0.00592691), DM(0.0158899), DM(0.0304973), DM(0.0497215), DM(0.0735265), DM(0.101867), DM(0.134691), DM(0.171935), DM(0.21353), DM(0.259397), DM(0.309451), DM(0.363596), DM(0.421732), DM(0.481659), DM(0.541586), DM(0.601514), DM(0.661441), DM(0.728391), DM(0.79534), DM(0.86229), DM(0.929239), DM(0.996189), DM(1.06314), DM(1.13009), DM(1.19704), DM(1.26399), DM(1.33094), DM(1.39789), DM(1.46484), DM(1.53178), DM(1.59873), DM(1.66568), DM(1.73263), DM(1.79958), DM(1.86653), DM(1.93348), DM(2.00043)]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y_Coordinates')"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "ang=[]\n",
    "v=[]\n",
    "t1=[]\n",
    "t2=[]\n",
    "for i in range(0,dt):\n",
    "    ang.append(out[0][i])\n",
    "v.append(out[0][dt])\n",
    "t1.append(out[0][dt+1])\n",
    "t2.append(out[0][dt+2])\n",
    "xc,yc=[],[]\n",
    "theta=0.0\n",
    "x1,x2=0,0\n",
    "for i in range(0,dt1):\n",
    "    x1=x1+v[0]*math.cos(theta)*t1[0]\n",
    "    x2=x2+v[0]*math.sin(theta)*t1[0]\n",
    "    theta = theta + (v[0] / 1.2) * math.tan(ang[i])*t1[0]\n",
    "    xc.append(x1)\n",
    "    yc.append(x2)\n",
    "print(x1,x2)\n",
    "for i in range(dt1,dt):\n",
    "    x1=x1+v[0]*math.cos(theta)*t2[0]\n",
    "    x2=x2+v[0]*math.sin(theta)*t2[0]\n",
    "    theta = theta + (v[0] / 1.2) * math.tan(ang[i])*t2[0]\n",
    "    xc.append(x1)\n",
    "    yc.append(x2)\n",
    "print(xc)\n",
    "print(yc)\n",
    "plt.plot(xc,yc,dest[0],dest[1],\"o\",way[0],way[1],\"D\",obs[0],obs[1],\"s\")\n",
    "plt.legend([\"path\", \"destination\",\"waypoint\",\"obstacle\"])\n",
    "plt.xlabel(\"X_Coordinates\")\n",
    "plt.ylabel(\"Y_Coordinates\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[], [DM(0.077201), DM(0)], [DM(0.154365), DM(0.00239878)], [DM(0.231417), DM(0.00719403)], [DM(0.308282), DM(0.0143811)], [DM(0.384888), DM(0.0239531)], [DM(0.461158), DM(0.0359007)], [DM(0.537021), DM(0.0502125)], [DM(0.612403), DM(0.0668745)], [DM(0.68723), DM(0.0858707)], [DM(0.761431), DM(0.107183)], [DM(0.835198), DM(0.129952)], [DM(0.908965), DM(0.152721)], [DM(0.982732), DM(0.17549)], [DM(1.0565), DM(0.198259)], [DM(1.13027), DM(0.221027)], [DM(1.20403), DM(0.243796)], [DM(1.2778), DM(0.266565)], [DM(1.35157), DM(0.289334)], [DM(1.42533), DM(0.312103)], [DM(1.49857), DM(0.336536)], [DM(1.55549), DM(0.357515)], [DM(1.61188), DM(0.379877)], [DM(1.66771), DM(0.403609)], [DM(1.72295), DM(0.428697)], [DM(1.77755), DM(0.455127)], [DM(1.83149), DM(0.482882)], [DM(1.88474), DM(0.511947)], [DM(1.93727), DM(0.542302)], [DM(1.98904), DM(0.573931)], [DM(2.04001), DM(0.606815)], [DM(2.09018), DM(0.640934)], [DM(2.13949), DM(0.676267)], [DM(2.18792), DM(0.712794)], [DM(2.23545), DM(0.750493)], [DM(2.28205), DM(0.789341)], [DM(2.32768), DM(0.829315)], [DM(2.37232), DM(0.870392)], [DM(2.41595), DM(0.912546)], [DM(2.45853), DM(0.955753)], [DM(2.50005), DM(0.999987)]]\n"
     ]
    }
   ],
   "source": [
    "temp1=[[]]\n",
    "for i in range(0,len(xc)):\n",
    "    temp1.append([xc[i],yc[i]])\n",
    "print(temp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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