cd V:\Cursos\Pos\Otimiza\Aulas type test17 function [S,G,H]=test(x) % Edgar & Himmelblau, 1988 % minimo local: % x0 = [1, 1]' ou x0 = [-1, -1]' % xo = [3.5355 3.5355]' ou xo = [-3.5355 -3.5355]' % S(xo) = 0 % minimo global: % x0 = [-1, 1]' ou x0 = [1, -1]' % xo = [-3.5355 3.5355]' ou xo = [3.5355 -3.5355]' % S(xo) = -12.5 %op=optimset('LargeScale','off','MaxFunEvals',inf,'MaxIter',200,'Display','iter',... % 'TolFun',1e-6,'TolX',1e-6,'LineSearch','quadcubic','TolCon',1e-6); %op=optimset('LargeScale','off','MaxFunEvals',inf,'MaxIter',200,'Display','iter',... % 'TolFun',1e-6,'TolX',1e-6,'LineSearch','quadcubic','TolCon',1e-6,'GradObj','on', ... % 'HessUpdate','bfgs'); %[x,S,ex,out,lambda,grad,hess]=fmincon('test17',[1 1],[],[],[],[],[],[],'restr17',op) S=x(1).*x(2); if nargout > 1 G=[x(2); x(1)]; % gradiente if nargout > 2 H = [0 1;1 0]; end end op=optimset('LargeScale','off','MaxFunEvals',inf,'MaxIter',200,'Display','iter',... 'TolFun',1e-6,'TolX',1e-6,'LineSearch','quadcubic','TolCon',1e-6); [x,S,ex,out,lambda,grad,hess]=fmincon('test17',[1 1],[],[],[],[],[],[],'restr17',op) max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 1 -23 1 -2 2 7 2.52482e-017 -25 1 -5.05e-017 Optimization terminated successfully: Search direction less than 2*options.TolX and maximum constraint violation is less than options.TolCon No Active Constraints x = 1.0e-016 * -0.2525 -0.2525 S = 6.3747e-034 ex = 1 out = iterations: 2 funcCount: 9 stepsize: 1 algorithm: 'medium-scale: SQP, Quasi-Newton, line-search' firstorderopt: [] cgiterations: [] lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [1x0 double] eqnonlin: [1x0 double] ineqlin: [1x0 double] ineqnonlin: 0 grad = 1.0e-008 * -0.5025 -0.5025 hess = 1.0000 0.0000 0.0000 1.0000 [x,S,ex,out,lambda,grad,hess]=fmincon('test17',[-1 -1],[],[],[],[],[],[],'restr17',op) max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 1 -23 1 -2 2 7 3.69357e-017 -25 1 -7.39e-017 Optimization terminated successfully: Search direction less than 2*options.TolX and maximum constraint violation is less than options.TolCon No Active Constraints x = 1.0e-016 * 0.3694 0.3694 S = 1.3642e-033 ex = 1 out = iterations: 2 funcCount: 9 stepsize: 1 algorithm: 'medium-scale: SQP, Quasi-Newton, line-search' firstorderopt: [] cgiterations: [] lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [1x0 double] eqnonlin: [1x0 double] ineqlin: [1x0 double] ineqnonlin: 0 grad = 1.0e-008 * -0.6077 -0.6077 hess = 1.0000 -0.0000 -0.0000 1.0000 [x,S,ex,out,lambda,grad,hess]=fmincon('test17',[-1 1],[],[],[],[],[],[],'restr17',op) max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 -1 -23 1 -2 2 7 -4 -17 1 -8.5 Hessian modified twice 3 11 -17.0156 9.031 1 4.52 Hessian modified twice 4 15 -12.7996 0.5992 1 0.3 5 19 -12.5018 0.003581 1 0.00179 Hessian modified 6 23 -12.5 1.286e-007 1 6.41e-008 Hessian modified Optimization terminated successfully: Magnitude of directional derivative in search direction less than 2*options.TolFun and maximum constraint violation is less than options.TolCon Active Constraints: 1 x = -3.5355 3.5355 S = -12.5000 ex = 1 out = iterations: 6 funcCount: 25 stepsize: 1 algorithm: 'medium-scale: SQP, Quasi-Newton, line-search' firstorderopt: [] cgiterations: [] lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [1x0 double] eqnonlin: [1x0 double] ineqlin: [1x0 double] ineqnonlin: 0.5000 grad = 3.5355 -3.5355 hess = 0.9387 0.9247 0.9247 0.9216 [x,S,ex,out,lambda,grad,hess]=fmincon('test17',[1 -1],[],[],[],[],[],[],'restr17',op) max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 -1 -23 1 -2 2 7 -4 -17 1 -8.5 Hessian modified twice 3 11 -17.0156 9.031 1 4.52 Hessian modified twice 4 15 -12.7996 0.5992 1 0.3 5 19 -12.5018 0.003581 1 0.00179 Hessian modified 6 23 -12.5 1.287e-007 1 6.41e-008 Hessian modified Optimization terminated successfully: Magnitude of directional derivative in search direction less than 2*options.TolFun and maximum constraint violation is less than options.TolCon Active Constraints: 1 x = 3.5355 -3.5355 S = -12.5000 ex = 1 out = iterations: 6 funcCount: 25 stepsize: 1 algorithm: 'medium-scale: SQP, Quasi-Newton, line-search' firstorderopt: [] cgiterations: [] lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [1x0 double] eqnonlin: [1x0 double] ineqlin: [1x0 double] ineqnonlin: 0.5000 grad = -3.5355 3.5355 hess = 0.9179 0.9214 0.9214 0.9361 help sqp Constrained optimization using SQP to solve problems like: min S(x) subject to: g(x) <= 0, h(x) = 0 (nonlinear constraints) x Lb <= x <= Ub [xo,Ot,nS,lambda]=sqp(S,Res,x0,ip,Gr,linesearch,Lb,Ub,problem,tol,mxit) S: objective function x0: initial point ip: (0) no plot (default), (>0) plot figure ip with pause, (<0) plot figure ip Res: contraint function returning [g(x),h(x)] Gr: gradient vector function of S(x) linesearch: (0) quadratic+cubic (default), (1) cubic Lb, Ub: lower and upper bound vectors [used also to plot (plot default = x0*(1+/-2))] problem: (-1): minimum (default), (1): maximum tol: tolerance (default = 1e-6) mxit: maximum number of iterations (default = 50*(1+4*~(ip>0))) xo: optimal point Ot: optimal value of S nS: number of objective function evaluations lambda: Lagrange multipliers [xo,Ot,nS,lambda]=sqp('test17','restr17',[1 1],1) Pause: hit any key to continue... max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 1 -23 1 -2 Pause: hit any key to continue... 2 7 2.52482e-017 -25 1 -5.05e-017 Optimization terminated successfully: Search direction less than 2*options.TolX and maximum constraint violation is less than options.TolCon No Active Constraints Pause: hit any key to continue... xo = 1.0e-016 * -0.2525 -0.2525 Ot = 6.3747e-034 nS = 9 lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [1x0 double] eqnonlin: [1x0 double] ineqlin: [1x0 double] ineqnonlin: 0 figure [xo,Ot,nS,lambda]=sqp('test17','restr17',[1 1],1) Pause: hit any key to continue... max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 1 -23 1 -2 Pause: hit any key to continue... 2 7 2.52482e-017 -25 1 -5.05e-017 Optimization terminated successfully: Search direction less than 2*options.TolX and maximum constraint violation is less than options.TolCon No Active Constraints Pause: hit any key to continue... xo = 1.0e-016 * -0.2525 -0.2525 Ot = 6.3747e-034 nS = 9 lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [1x0 double] eqnonlin: [1x0 double] ineqlin: [1x0 double] ineqnonlin: 0 [xo,Ot,nS,lambda]=sqp('test17','restr17',[1 1],2) Pause: hit any key to continue... max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 1 -23 1 -2 Pause: hit any key to continue... 2 7 2.52482e-017 -25 1 -5.05e-017 Optimization terminated successfully: Search direction less than 2*options.TolX and maximum constraint violation is less than options.TolCon No Active Constraints Pause: hit any key to continue... xo = 1.0e-016 * -0.2525 -0.2525 Ot = 6.3747e-034 nS = 9 lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [1x0 double] eqnonlin: [1x0 double] ineqlin: [1x0 double] ineqnonlin: 0 [xo,Ot,nS,lambda]=sqp('test17','restr17',[1 1],-2) max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 1 -23 1 -2 2 7 2.52482e-017 -25 1 -5.05e-017 Optimization terminated successfully: Search direction less than 2*options.TolX and maximum constraint violation is less than options.TolCon No Active Constraints xo = 1.0e-016 * -0.2525 -0.2525 Ot = 6.3747e-034 nS = 9 lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [1x0 double] eqnonlin: [1x0 double] ineqlin: [1x0 double] ineqnonlin: 0 help sqp Constrained optimization using SQP to solve problems like: min S(x) subject to: g(x) <= 0, h(x) = 0 (nonlinear constraints) x Lb <= x <= Ub [xo,Ot,nS,lambda]=sqp(S,Res,x0,ip,Gr,linesearch,Lb,Ub,problem,tol,mxit) S: objective function x0: initial point ip: (0) no plot (default), (>0) plot figure ip with pause, (<0) plot figure ip Res: contraint function returning [g(x),h(x)] Gr: gradient vector function of S(x) linesearch: (0) quadratic+cubic (default), (1) cubic Lb, Ub: lower and upper bound vectors [used also to plot (plot default = x0*(1+/-2))] problem: (-1): minimum (default), (1): maximum tol: tolerance (default = 1e-6) mxit: maximum number of iterations (default = 50*(1+4*~(ip>0))) xo: optimal point Ot: optimal value of S nS: number of objective function evaluations lambda: Lagrange multipliers [xo,Ot,nS,lambda]=sqp('test17','restr17',[1 1],-1,'gtest17',[],[-5 -5],[5 5]) max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 1 -4 1 -2 2 7 0 -5 1 0 Optimization terminated successfully: Search direction less than 2*options.TolX and maximum constraint violation is less than options.TolCon No Active Constraints xo = 0 0 Ot = 0 nS = 9 lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [0x1 double] eqnonlin: [0x1 double] ineqlin: [0x1 double] ineqnonlin: 0 [xo,Ot,nS,lambda]=sqp('test17','restr17',[-1 1],-1,'gtest17',[],[-5 -5],[5 5]) max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 -1 -4 1 -2 2 7 -4 -3 1 -8.5 Hessian modified twice 3 11 -17.0156 9.031 1 4.52 Hessian modified twice 4 15 -12.7996 0.5992 1 0.3 5 19 -12.5018 0.003583 1 0.00179 Hessian modified 6 23 -12.5 1.283e-007 1 6.41e-008 Hessian modified Optimization terminated successfully: Search direction less than 2*options.TolX and maximum constraint violation is less than options.TolCon Active Constraints: 5 xo = -3.5355 3.5355 Ot = -12.5000 nS = 25 lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [0x1 double] eqnonlin: [0x1 double] ineqlin: [0x1 double] ineqnonlin: 0.5000 [xo,Ot,nS,lambda]=sqp('test17','restr17',[1 -1],-1,'gtest17',[],[-5 -5],[5 5]) max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 -1 -4 1 -2 2 7 -4 -3 1 -8.5 Hessian modified twice 3 11 -17.0156 9.031 1 4.52 Hessian modified twice 4 15 -12.7996 0.5992 1 0.3 5 19 -12.5018 0.003583 1 0.00179 Hessian modified 6 23 -12.5 1.284e-007 1 6.42e-008 Hessian modified Optimization terminated successfully: Search direction less than 2*options.TolX and maximum constraint violation is less than options.TolCon Active Constraints: 5 xo = 3.5355 -3.5355 Ot = -12.5000 nS = 25 lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [0x1 double] eqnonlin: [0x1 double] ineqlin: [0x1 double] ineqnonlin: 0.5000 [xo,Ot,nS,lambda]=sqp('test17','restr17',[1 1],-1,'gtest17',[],[-5 -5],[5 5]) max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 1 -4 1 -2 2 7 0 -5 1 0 Optimization terminated successfully: Search direction less than 2*options.TolX and maximum constraint violation is less than options.TolCon No Active Constraints xo = 0 0 Ot = 0 nS = 9 lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [0x1 double] eqnonlin: [0x1 double] ineqlin: [0x1 double] ineqnonlin: 0 [xo,Ot,nS,lambda]=sqp('test17','restr17',[-1 -1],-1,'gtest17',[],[-5 -5],[5 5]) max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 1 -4 1 -2 2 7 0 -5 1 0 Optimization terminated successfully: Search direction less than 2*options.TolX and maximum constraint violation is less than options.TolCon No Active Constraints xo = 0 0 Ot = 0 nS = 9 lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [0x1 double] eqnonlin: [0x1 double] ineqlin: [0x1 double] ineqnonlin: 0 help sqp Constrained optimization using SQP to solve problems like: min S(x) subject to: g(x) <= 0, h(x) = 0 (nonlinear constraints) x Lb <= x <= Ub [xo,Ot,nS,lambda]=sqp(S,Res,x0,ip,Gr,linesearch,Lb,Ub,problem,tol,mxit) S: objective function x0: initial point ip: (0) no plot (default), (>0) plot figure ip with pause, (<0) plot figure ip Res: contraint function returning [g(x),h(x)] Gr: gradient vector function of S(x) linesearch: (0) quadratic+cubic (default), (1) cubic Lb, Ub: lower and upper bound vectors [used also to plot (plot default = x0*(1+/-2))] problem: (-1): minimum (default), (1): maximum tol: tolerance (default = 1e-6) mxit: maximum number of iterations (default = 50*(1+4*~(ip>0))) xo: optimal point Ot: optimal value of S nS: number of objective function evaluations lambda: Lagrange multipliers [xo,Ot,nS,lambda]=sqp('test17','restr17',[1 1],-1,'gtest17',[],[-5 -5],[5 5],1) max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 -1 -4 1 -2 2 7 -4 -3 1 -8.5 Hessian modified twice 3 11 -17.0156 9.031 1 4.52 Hessian modified twice 4 15 -12.7996 0.5992 1 0.3 5 19 -12.5018 0.003583 1 0.00179 Hessian modified 6 23 -12.5 1.283e-007 1 6.42e-008 Hessian modified Optimization terminated successfully: Search direction less than 2*options.TolX and maximum constraint violation is less than options.TolCon Active Constraints: 5 xo = 3.5355 3.5355 Ot = 12.5000 nS = 25 lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [0x1 double] eqnonlin: [0x1 double] ineqlin: [0x1 double] ineqnonlin: 0.5000 [xo,Ot,nS,lambda]=sqp('test17','restr17',[-1 -1],-1,'gtest17',[],[-5 -5],[5 5],1) max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 -1 -4 1 -2 2 7 -4 -3 1 -8.5 Hessian modified twice 3 11 -17.0156 9.031 1 4.52 Hessian modified twice 4 15 -12.7996 0.5992 1 0.3 5 19 -12.5018 0.003583 1 0.00179 Hessian modified 6 23 -12.5 1.283e-007 1 6.42e-008 Hessian modified Optimization terminated successfully: Search direction less than 2*options.TolX and maximum constraint violation is less than options.TolCon Active Constraints: 5 xo = -3.5355 -3.5355 Ot = 12.5000 nS = 25 lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [0x1 double] eqnonlin: [0x1 double] ineqlin: [0x1 double] ineqnonlin: 0.5000 [xo,Ot,nS,lambda]=sqp('test17','restr17',[-1 -1],-1,[],[],[-5 -5],[5 5],1) max Directional Iter F-count f(x) constraint Step-size derivative Procedure 1 3 -1 -4 1 -2 2 7 -4 -3 1 -8.5 Hessian modified twice 3 11 -17.0156 9.031 1 4.52 Hessian modified twice 4 15 -12.7996 0.5992 1 0.3 5 19 -12.5018 0.003582 1 0.00179 Hessian modified 6 23 -12.5 1.283e-007 1 6.41e-008 Hessian modified Optimization terminated successfully: Search direction less than 2*options.TolX and maximum constraint violation is less than options.TolCon Active Constraints: 5 xo = -3.5355 -3.5355 Ot = 12.5000 nS = 25 lambda = lower: [2x1 double] upper: [2x1 double] eqlin: [0x1 double] eqnonlin: [0x1 double] ineqlin: [0x1 double] ineqnonlin: 0.5000 help grg Constrained optimization using GRG to solve problems like: min S(x) subject to: g(x) <= 0, h(x) = 0 (nonlinear constraints) x Lb <= x <= Ub [xo,Ot,nS,lambda]=grg(S,Res,x0,iXI,ip,GrS,GrR,Lb,Ub,problem,almax,opt) S: objective function x0: initial point ixI: index vector of independent variables (default: slack variables + first ones) ip: (0) no plot (default), (>0) plot figure ip with pause, (<0) plot figure ip Res: contraint function returning [g(x),h(x)] GrS: gradient vector function of S(x) GrR: gradient matrix function of Res(x), returning [Dg(x),Dh(x)] Lb, Ub: lower and upper bound vectors [used also to plot (plot default = x0*(1+/-2))] problem: (-1): minimum (default), (1): maximum almax: maximum stepsize (default = 10) opt: options vector set using optimset('grg') xo: optimal point Ot: optimal value of S nS: number of objective function evaluations lambda: Lagrange multipliers [xo,Ot,nS,lambda]=grg('test17','restr17',[-1 -1],[1 2],-1,'gtest17','grestr17',[-5 -5],[5 5]) max Directional Call Iter F-count f(x) constraint Step-size derivative 1 50 1.97215e-031 0 0.5 -2 0 2 99 3.88938e-062 0 0.5 -3.94e-031 0 xo = 1.0e-030 * -0.1972 -0.1972 Ot = 3.8894e-062 nS = 99 lambda = 0 [xo,Ot,nS,lambda]=grg('test17','restr17',[1 1],[1 2],-1,'gtest17','grestr17',[-5 -5],[5 5]) max Directional Call Iter F-count f(x) constraint Step-size derivative 1 50 1.97215e-031 0 0.5 -2 0 2 99 3.88938e-062 0 0.5 -3.94e-031 0 xo = 1.0e-030 * 0.1972 0.1972 Ot = 3.8894e-062 nS = 99 lambda = 0 [xo,Ot,nS,lambda]=grg('test17','restr17',[-1 1],[1 2],-1,'gtest17','grestr17',[-5 -5],[5 5]) max Directional Call Iter F-count f(x) constraint Step-size derivative 1 40 -9 0 1 -2 0 2 79 -10.8351 0 0.0486 -18 0 3 118 -11.6835 0 0.0192 -21.7 0 4 157 -12.0953 0 0.00874 -23.4 0 5 196 -12.2985 0 0.00418 -24.2 0 6 235 -12.3995 0 0.00205 -24.6 0 7 274 -12.4498 0 0.00101 -24.8 0 8 313 -12.4749 0 0.000504 -24.9 0 9 352 -12.4875 0 0.000251 -24.9 0 10 391 -12.4937 0 0.000126 -25 0 11 430 -12.4969 0 6.27e-005 -25 0 12 468 -12.5 3.934e-007 6.27e-005 -25 0 13 505 -12.5 3.934e-007 0.414 -25 1 14 571 -12.5 3.934e-007 2.93e-015 -0.25 1 15 858 -12.5 3.934e-007 4.35e-082 -0.25 1 16 924 -12.5 3.934e-007 1.47e-015 -0.25 1 17 992 -12.5 3.934e-007 3.66e-016 -0.25 1 18 1061 -12.5 3.934e-007 1.83e-016 -0.25 1 19 1131 -12.5 3.934e-007 9.16e-017 -0.25 1 20 1202 -12.5 3.934e-007 4.58e-017 -0.25 1 21 1274 -12.5 3.934e-007 2.29e-017 -0.25 1 22 1347 -12.5 3.934e-007 1.14e-017 -0.25 1 23 1422 -12.5 3.934e-007 2.86e-018 -0.25 1 24 1498 -12.5 3.934e-007 1.43e-018 -0.25 1 25 1576 -12.5 3.934e-007 3.58e-019 -0.25 1 26 1655 -12.5 3.934e-007 1.79e-019 -0.25 1 27 1736 -12.5 3.934e-007 4.47e-020 -0.25 1 28 1818 -12.5 3.934e-007 2.24e-020 -0.25 1 29 1901 -12.5 3.934e-007 1.12e-020 -0.25 1 30 1985 -12.5 3.934e-007 5.59e-021 -0.25 1 31 2070 -12.5 3.934e-007 2.8e-021 -0.25 1 32 2156 -12.5 3.934e-007 1.4e-021 -0.25 1 33 2243 -12.5 3.934e-007 6.99e-022 -0.25 1 34 2331 -12.5 3.934e-007 3.49e-022 -0.25 1 35 2423 -12.5 3.934e-007 2.18e-023 -0.25 1 36 2515 -12.5 3.934e-007 2.18e-023 -0.25 1 37 2607 -12.5 3.934e-007 2.18e-023 -0.25 1 38 2699 -12.5 3.934e-007 2.18e-023 -0.25 1 39 2791 -12.5 3.934e-007 2.18e-023 -0.25 1 40 2883 -12.5 3.934e-007 2.18e-023 -0.25 1 41 2975 -12.5 3.934e-007 2.18e-023 -0.25 1 42 3067 -12.5 3.934e-007 2.18e-023 -0.25 1 43 3159 -12.5 3.934e-007 2.18e-023 -0.25 1 44 3251 -12.5 3.934e-007 2.18e-023 -0.25 1 45 3343 -12.5 3.934e-007 2.18e-023 -0.25 1 46 3435 -12.5 3.934e-007 2.18e-023 -0.25 1 47 3527 -12.5 3.934e-007 2.18e-023 -0.25 1 48 3619 -12.5 3.934e-007 2.18e-023 -0.25 1 49 3711 -12.5 3.934e-007 2.18e-023 -0.25 1 50 3803 -12.5 3.934e-007 2.18e-023 -0.25 1 51 3895 -12.5 3.934e-007 2.18e-023 -0.25 1 52 3987 -12.5 3.934e-007 2.18e-023 -0.25 1 53 4079 -12.5 3.934e-007 2.18e-023 -0.25 1 54 4171 -12.5 3.934e-007 2.18e-023 -0.25 1 55 4263 -12.5 3.934e-007 2.18e-023 -0.25 1 56 4355 -12.5 3.934e-007 2.18e-023 -0.25 1 57 4447 -12.5 3.934e-007 2.18e-023 -0.25 1 58 4539 -12.5 3.934e-007 2.18e-023 -0.25 1 59 4631 -12.5 3.934e-007 2.18e-023 -0.25 1 60 4723 -12.5 3.934e-007 2.18e-023 -0.25 1 61 4815 -12.5 3.934e-007 2.18e-023 -0.25 1 62 4907 -12.5 3.934e-007 2.18e-023 -0.25 1 63 4999 -12.5 3.934e-007 2.18e-023 -0.25 1 64 5091 -12.5 3.934e-007 2.18e-023 -0.25 1 65 5183 -12.5 3.934e-007 2.18e-023 -0.25 1 66 5275 -12.5 3.934e-007 2.18e-023 -0.25 1 67 5367 -12.5 3.934e-007 2.18e-023 -0.25 1 68 5459 -12.5 3.934e-007 2.18e-023 -0.25 1 69 5551 -12.5 3.934e-007 2.18e-023 -0.25 1 70 5643 -12.5 3.934e-007 2.18e-023 -0.25 1 71 5735 -12.5 3.934e-007 2.18e-023 -0.25 1 72 5827 -12.5 3.934e-007 2.18e-023 -0.25 1 73 5919 -12.5 3.934e-007 2.18e-023 -0.25 1 74 6011 -12.5 3.934e-007 2.18e-023 -0.25 1 75 6103 -12.5 3.934e-007 2.18e-023 -0.25 1 76 6195 -12.5 3.934e-007 2.18e-023 -0.25 1 77 6287 -12.5 3.934e-007 2.18e-023 -0.25 1 78 6379 -12.5 3.934e-007 2.18e-023 -0.25 1 79 6471 -12.5 3.934e-007 2.18e-023 -0.25 1 80 6563 -12.5 3.934e-007 2.18e-023 -0.25 1 81 6655 -12.5 3.934e-007 2.18e-023 -0.25 1 82 6747 -12.5 3.934e-007 2.18e-023 -0.25 1 83 6839 -12.5 3.934e-007 2.18e-023 -0.25 1 84 6931 -12.5 3.934e-007 2.18e-023 -0.25 1 85 7023 -12.5 3.934e-007 2.18e-023 -0.25 1 86 7115 -12.5 3.934e-007 2.18e-023 -0.25 1 87 7207 -12.5 3.934e-007 2.18e-023 -0.25 1 88 7299 -12.5 3.934e-007 2.18e-023 -0.25 1 89 7391 -12.5 3.934e-007 2.18e-023 -0.25 1 90 7483 -12.5 3.934e-007 2.18e-023 -0.25 1 91 7575 -12.5 3.934e-007 2.18e-023 -0.25 1 92 7667 -12.5 3.934e-007 2.18e-023 -0.25 1 93 7759 -12.5 3.934e-007 2.18e-023 -0.25 1 94 7851 -12.5 3.934e-007 2.18e-023 -0.25 1 95 7943 -12.5 3.934e-007 2.18e-023 -0.25 1 96 8035 -12.5 3.934e-007 2.18e-023 -0.25 1 97 8127 -12.5 3.934e-007 2.18e-023 -0.25 1 98 8219 -12.5 3.934e-007 2.18e-023 -0.25 1 99 8311 -12.5 3.934e-007 2.18e-023 -0.25 1 100 8403 -12.5 3.934e-007 2.18e-023 -0.25 1 101 8495 -12.5 3.934e-007 2.18e-023 -0.25 1 102 8587 -12.5 3.934e-007 2.18e-023 -0.25 1 103 8679 -12.5 3.934e-007 2.18e-023 -0.25 1 104 8771 -12.5 3.934e-007 2.18e-023 -0.25 1 105 8863 -12.5 3.934e-007 2.18e-023 -0.25 1 106 8955 -12.5 3.934e-007 2.18e-023 -0.25 1 107 9047 -12.5 3.934e-007 2.18e-023 -0.25 1 108 9139 -12.5 3.934e-007 2.18e-023 -0.25 1 109 9231 -12.5 3.934e-007 2.18e-023 -0.25 1 110 9323 -12.5 3.934e-007 2.18e-023 -0.25 1 111 9415 -12.5 3.934e-007 2.18e-023 -0.25 1 112 9507 -12.5 3.934e-007 2.18e-023 -0.25 1 113 9599 -12.5 3.934e-007 2.18e-023 -0.25 1 114 9691 -12.5 3.934e-007 2.18e-023 -0.25 1 115 9783 -12.5 3.934e-007 2.18e-023 -0.25 1 116 9875 -12.5 3.934e-007 2.18e-023 -0.25 1 117 9967 -12.5 3.934e-007 2.18e-023 -0.25 1 118 10059 -12.5 3.934e-007 2.18e-023 -0.25 1 119 10151 -12.5 3.934e-007 2.18e-023 -0.25 1 120 10243 -12.5 3.934e-007 2.18e-023 -0.25 1 121 10335 -12.5 3.934e-007 2.18e-023 -0.25 1 122 10427 -12.5 3.934e-007 2.18e-023 -0.25 1 123 10519 -12.5 3.934e-007 2.18e-023 -0.25 1 124 10611 -12.5 3.934e-007 2.18e-023 -0.25 1 125 10703 -12.5 3.934e-007 2.18e-023 -0.25 1 126 10795 -12.5 3.934e-007 2.18e-023 -0.25 1 127 10887 -12.5 3.934e-007 2.18e-023 -0.25 1 128 10979 -12.5 3.934e-007 2.18e-023 -0.25 1 129 11071 -12.5 3.934e-007 2.18e-023 -0.25 1 130 11163 -12.5 3.934e-007 2.18e-023 -0.25 1 131 11255 -12.5 3.934e-007 2.18e-023 -0.25 1 132 11347 -12.5 3.934e-007 2.18e-023 -0.25 1 133 11439 -12.5 3.934e-007 2.18e-023 -0.25 1 134 11531 -12.5 3.934e-007 2.18e-023 -0.25 1 135 11623 -12.5 3.934e-007 2.18e-023 -0.25 1 136 11715 -12.5 3.934e-007 2.18e-023 -0.25 1 137 11807 -12.5 3.934e-007 2.18e-023 -0.25 1 138 11899 -12.5 3.934e-007 2.18e-023 -0.25 1 139 11991 -12.5 3.934e-007 2.18e-023 -0.25 1 140 12083 -12.5 3.934e-007 2.18e-023 -0.25 1 141 12175 -12.5 3.934e-007 2.18e-023 -0.25 1 142 12267 -12.5 3.934e-007 2.18e-023 -0.25 1 143 12359 -12.5 3.934e-007 2.18e-023 -0.25 1 144 12451 -12.5 3.934e-007 2.18e-023 -0.25 1 145 12543 -12.5 3.934e-007 2.18e-023 -0.25 1 146 12635 -12.5 3.934e-007 2.18e-023 -0.25 1 147 12727 -12.5 3.934e-007 2.18e-023 -0.25 1 148 12819 -12.5 3.934e-007 2.18e-023 -0.25 1 149 12911 -12.5 3.934e-007 2.18e-023 -0.25 1 150 13003 -12.5 3.934e-007 2.18e-023 -0.25 1 151 13095 -12.5 3.934e-007 2.18e-023 -0.25 1 152 13187 -12.5 3.934e-007 2.18e-023 -0.25 1 153 13279 -12.5 3.934e-007 2.18e-023 -0.25 1 154 13371 -12.5 3.934e-007 2.18e-023 -0.25 1 155 13463 -12.5 3.934e-007 2.18e-023 -0.25 1 156 13555 -12.5 3.934e-007 2.18e-023 -0.25 1 157 13647 -12.5 3.934e-007 2.18e-023 -0.25 1 158 13739 -12.5 3.934e-007 2.18e-023 -0.25 1 159 13831 -12.5 3.934e-007 2.18e-023 -0.25 1 160 13923 -12.5 3.934e-007 2.18e-023 -0.25 1 161 14015 -12.5 3.934e-007 2.18e-023 -0.25 1 162 14107 -12.5 3.934e-007 2.18e-023 -0.25 1 163 14199 -12.5 3.934e-007 2.18e-023 -0.25 1 164 14291 -12.5 3.934e-007 2.18e-023 -0.25 1 165 14383 -12.5 3.934e-007 2.18e-023 -0.25 1 166 14475 -12.5 3.934e-007 2.18e-023 -0.25 1 167 14567 -12.5 3.934e-007 2.18e-023 -0.25 1 168 14659 -12.5 3.934e-007 2.18e-023 -0.25 1 169 14751 -12.5 3.934e-007 2.18e-023 -0.25 1 170 14843 -12.5 3.934e-007 2.18e-023 -0.25 1 171 14935 -12.5 3.934e-007 2.18e-023 -0.25 1 172 15027 -12.5 3.934e-007 2.18e-023 -0.25 1 173 15119 -12.5 3.934e-007 2.18e-023 -0.25 1 174 15211 -12.5 3.934e-007 2.18e-023 -0.25 1 175 15303 -12.5 3.934e-007 2.18e-023 -0.25 1 176 15395 -12.5 3.934e-007 2.18e-023 -0.25 1 177 15487 -12.5 3.934e-007 2.18e-023 -0.25 1 178 15579 -12.5 3.934e-007 2.18e-023 -0.25 1 179 15671 -12.5 3.934e-007 2.18e-023 -0.25 1 180 15763 -12.5 3.934e-007 2.18e-023 -0.25 1 181 15855 -12.5 3.934e-007 2.18e-023 -0.25 1 182 15947 -12.5 3.934e-007 2.18e-023 -0.25 1 183 16039 -12.5 3.934e-007 2.18e-023 -0.25 1 184 16131 -12.5 3.934e-007 2.18e-023 -0.25 1 185 16223 -12.5 3.934e-007 2.18e-023 -0.25 1 186 16315 -12.5 3.934e-007 2.18e-023 -0.25 1 187 16407 -12.5 3.934e-007 2.18e-023 -0.25 1 188 16499 -12.5 3.934e-007 2.18e-023 -0.25 1 189 16591 -12.5 3.934e-007 2.18e-023 -0.25 1 190 16683 -12.5 3.934e-007 2.18e-023 -0.25 1 191 16775 -12.5 3.934e-007 2.18e-023 -0.25 1 192 16867 -12.5 3.934e-007 2.18e-023 -0.25 1 193 16959 -12.5 3.934e-007 2.18e-023 -0.25 1 194 17051 -12.5 3.934e-007 2.18e-023 -0.25 1 195 17143 -12.5 3.934e-007 2.18e-023 -0.25 1 196 17235 -12.5 3.934e-007 2.18e-023 -0.25 1 197 17327 -12.5 3.934e-007 2.18e-023 -0.25 1 198 17419 -12.5 3.934e-007 2.18e-023 -0.25 1 199 17511 -12.5 3.934e-007 2.18e-023 -0.25 1 200 17603 -12.5 3.934e-007 2.18e-023 -0.25 1 201 17695 -12.5 3.934e-007 2.18e-023 -0.25 1 202 17787 -12.5 3.934e-007 2.18e-023 -0.25 1 203 17879 -12.5 3.934e-007 2.18e-023 -0.25 1 204 17971 -12.5 3.934e-007 2.18e-023 -0.25 1 205 18063 -12.5 3.934e-007 2.18e-023 -0.25 1 206 18155 -12.5 3.934e-007 2.18e-023 -0.25 1 207 18247 -12.5 3.934e-007 2.18e-023 -0.25 1 208 18339 -12.5 3.934e-007 2.18e-023 -0.25 1 209 18431 -12.5 3.934e-007 2.18e-023 -0.25 1 210 18523 -12.5 3.934e-007 2.18e-023 -0.25 1 211 18615 -12.5 3.934e-007 2.18e-023 -0.25 1 212 18707 -12.5 3.934e-007 2.18e-023 -0.25 1 213 18799 -12.5 3.934e-007 2.18e-023 -0.25 1 214 18891 -12.5 3.934e-007 2.18e-023 -0.25 1 215 18983 -12.5 3.934e-007 2.18e-023 -0.25 1 216 19075 -12.5 3.934e-007 2.18e-023 -0.25 1 217 19167 -12.5 3.934e-007 2.18e-023 -0.25 1 218 19259 -12.5 3.934e-007 2.18e-023 -0.25 1 219 19351 -12.5 3.934e-007 2.18e-023 -0.25 1 220 19443 -12.5 3.934e-007 2.18e-023 -0.25 1 221 19535 -12.5 3.934e-007 2.18e-023 -0.25 1 222 19627 -12.5 3.934e-007 2.18e-023 -0.25 1 223 19719 -12.5 3.934e-007 2.18e-023 -0.25 1 224 19811 -12.5 3.934e-007 2.18e-023 -0.25 1 225 19903 -12.5 3.934e-007 2.18e-023 -0.25 1 226 19995 -12.5 3.934e-007 2.18e-023 -0.25 1 227 20087 -12.5 3.934e-007 2.18e-023 -0.25 1 228 20179 -12.5 3.934e-007 2.18e-023 -0.25 1 229 20271 -12.5 3.934e-007 2.18e-023 -0.25 1 230 20363 -12.5 3.934e-007 2.18e-023 -0.25 1 231 20455 -12.5 3.934e-007 2.18e-023 -0.25 1 232 20547 -12.5 3.934e-007 2.18e-023 -0.25 1 233 20639 -12.5 3.934e-007 2.18e-023 -0.25 1 234 20731 -12.5 3.934e-007 2.18e-023 -0.25 1 235 20823 -12.5 3.934e-007 2.18e-023 -0.25 1 236 20915 -12.5 3.934e-007 2.18e-023 -0.25 1 237 21007 -12.5 3.934e-007 2.18e-023 -0.25 1 238 21099 -12.5 3.934e-007 2.18e-023 -0.25 1 239 21191 -12.5 3.934e-007 2.18e-023 -0.25 1 240 21283 -12.5 3.934e-007 2.18e-023 -0.25 1 241 21375 -12.5 3.934e-007 2.18e-023 -0.25 1 242 21467 -12.5 3.934e-007 2.18e-023 -0.25 1 243 21559 -12.5 3.934e-007 2.18e-023 -0.25 1 244 21651 -12.5 3.934e-007 2.18e-023 -0.25 1 245 21743 -12.5 3.934e-007 2.18e-023 -0.25 1 246 21835 -12.5 3.934e-007 2.18e-023 -0.25 1 247 21927 -12.5 3.934e-007 2.18e-023 -0.25 1 248 22019 -12.5 3.934e-007 2.18e-023 -0.25 1 249 22111 -12.5 3.934e-007 2.18e-023 -0.25 1 250 22203 -12.5 3.934e-007 2.18e-023 -0.25 1 Warning GRG: reached maximum number of iterations! xo = -3.5355 3.5355 Ot = -12.5000 nS = 22203 lambda = 0.5000