Matlab optimprob _Problem based _how can i see the optimal parameter setting while using optimprob

Hi,

i am using optimprob for grid optimal dispatch problem, in this case how can i see the optimal parameter settings/iterations etc as we normally plot for other optimization methods, ?

Any graph/data that i can plot during simulation like optimization search space/paramters etc using any commands ???

[values,~,exitflag] = solve(prob,'Options',options);

I use this command,

how can i see the optimal values and cosntraints

Please advcie

Answer 1

[Alan Weiss]

You can set a plot function in the options for your solver. To see which solver is being used, call optimoptions:

options = optimoptions(prob) % I assume prob is your optimproblem

Suppose that your solver is ga. Then you can set a plot function that ga accepts:

options = optimoptions(prob,"PlotFcn","gaplotbestf");

If the plot functions do not show what you want, feel free to write a custom plot function (ga is shown, but there are custom plot functions available for most solvers).

Alan Weiss

MATLAB mathematical toolbox documentation

[NN]

thank you very much .I will check this way

[NN]

mine is a problem based optimization, so how can i plot the same .Please advice

[NN]

This is my code ,

how can i view the optimal value, the variables that give the optimal value and the operating conditions?

% Solve the linear program

options = optimoptions(prob.optimoptions,'Display','none');

[values,~,exitflag] = solve(prob,'Options',options);

% Parse optmization results

if exitflag <= 0

Pd = zeros(N,1);

Pbattups = zeros(N,1);

Ebattups = zeros(N,1);

else

Pd = values.PdV;

Pbattups = values.PbattupsV;

Ebattups = values.EbattupsV;

end

[NN]

with below command It doesnt not display anything either in workspace or in command window , i dont knwo what mistake i have done.

options = optimoptions(prob.optimoptions,'Display','iter');

[NN]

when i used the below command

options = optimoptions(prob.optimoptions,'Display','iter');

[values,~,exitflag] = solve(prob,'Options',options);

fval = evaluate(prob.Objective,values);

I am getting below data in diagnosis viewer.

LP preprocessing removed 723 inequalities, 244 equalities,

727 variables, and 1379 non-zero elements.

Iter Time Fval Primal Infeas Dual Infeas

0 0.003 -5.713331e+09 5.951959e+08 0.000000e+00

101 0.004 -5.567856e+09 2.816612e+09 0.000000e+00

202 0.006 -5.397463e+09 1.412637e+10 0.000000e+00

303 0.007 -5.188584e+09 2.415490e+08 0.000000e+00

345 0.008 -5.186765e+09 0.000000e+00 0.000000e+00

Optimal solution found.

fval =

-6.0203e+09

What does thsi mean?what are the different parameters mentioned ?Please advice

In this below exampl,

https://www.mathworks.com/videos/mathematical-modeling-with-optimization-part-2a-problem-based-linear-programming-1500391996125.html

tb1=struct2table(chemsol)

gives the variable values for fval,

I rewritten it as

tb1=struct2table(values) and got 241*5 table in diagnostic viewer.

LP preprocessing removed 723 inequalities, 244 equalities,

727 variables, and 1379 non-zero elements.

Iter Time Fval Primal Infeas Dual Infeas

0 0.004 -5.713331e+09 5.951959e+08 0.000000e+00

101 0.005 -5.567856e+09 2.816612e+09 0.000000e+00

202 0.008 -5.397463e+09 1.412637e+10 0.000000e+00

303 0.011 -5.188584e+09 2.415490e+08 0.000000e+00

345 0.012 -5.186765e+09 0.000000e+00 0.000000e+00

Optimal solution found.

tb1 =

241×5 table

Ebatt1V EbattupsV PbattEVregV PbattupsV PdV

________ _________ ___________ ___________ ___________

7.2e+07 5.575e+09 -16500 -4.1667e+05 4.5438e+05

7.2e+07 5.7e+09 -16500 -5e+05 5.378e+05

7.2e+07 5.85e+09 -16500 -5e+05 5.3788e+05

7.2e+07 6e+09 -16500 5e+05 -4.6813e+05

7.2e+07 5.85e+09 -16500 5e+05 -4.683e+05

7.2e+07 5.7e+09 -16500 5e+05 -4.6846e+05

7.2e+07 5.55e+09 -16500 5e+05 -4.6862e+05

7.2e+07 5.4e+09 -16500 5e+05 -4.6879e+05

7.2e+07 5.25e+09 -16500 5e+05 -4.6895e+05

7.2e+07 5.1e+09 -16500 5e+05 -4.6911e+05

7.2e+07 4.95e+09 -16500 5e+05 -4.6927e+05

7.2e+07 4.8e+09 -16500 5e+05 -4.6944e+05

7.2e+07 4.65e+09 -16500 5e+05 -4.696e+05

7.2e+07 4.5e+09 -16500 5e+05 -4.6976e+05

7.2e+07 4.35e+09 -16500 5e+05 -4.6993e+05

7.2e+07 4.2e+09 -16500 5e+05 -4.7007e+05

7.2e+07 4.05e+09 -16500 5e+05 -4.7015e+05

7.2e+07 3.9e+09 -16500 5e+05 -4.7023e+05

7.2e+07 3.75e+09 -16500 5e+05 -4.7031e+05

7.2e+07 3.6e+09 -16500 5e+05 -4.7039e+05

7.2e+07 3.45e+09 -16500 5e+05 -4.7048e+05

7.2e+07 3.3e+09 -16500 5e+05 -4.7056e+05

7.2e+07 3.15e+09 -16500 5e+05 -4.7064e+05

7.2e+07 3e+09 -16500 5e+05 -4.7072e+05

7.2e+07 2.85e+09 -16500 5e+05 -4.708e+05

7.2e+07 2.7e+09 -16500 5e+05 -4.7088e+05

7.2e+07 2.55e+09 -16500 5e+05 -4.7096e+05

7.2e+07 2.4e+09 -16500 5e+05 -4.7102e+05

7.2e+07 2.25e+09 -16500 5e+05 -4.7099e+05

7.2e+07 2.1e+09 -16500 5e+05 -4.7097e+05

7.2e+07 1.95e+09 -16500 5e+05 -4.7094e+05

7.2e+07 1.8e+09 -16500 0 29084

7.2e+07 1.8e+09 -16500 0 29109

7.2e+07 1.8e+09 -16500 0 29134

7.2e+07 1.8e+09 -16500 0 29160

7.2e+07 1.8e+09 -16500 0 29185

7.2e+07 1.8e+09 -16500 0 29210

7.2e+07 1.8e+09 -16500 0 29235

7.2e+07 1.8e+09 -16500 0 29261

7.2e+07 1.8e+09 -16500 0 29302

7.2e+07 1.8e+09 -16500 0 29404

7.2e+07 1.8e+09 -16500 0 29506

7.2e+07 1.8e+09 -16500 0 29608

7.2e+07 1.8e+09 -16500 0 29710

7.2e+07 1.8e+09 -16500 0 29812

7.2e+07 1.8e+09 -16500 0 29914

7.2e+07 1.8e+09 -16500 0 30016

7.2e+07 1.8e+09 -16500 0 30119

7.2e+07 1.8e+09 -16500 0 30221

7.2e+07 1.8e+09 -16500 0 30323

7.2e+07 1.8e+09 -16500 0 30425

7.2e+07 1.8e+09 -16500 0 30643

7.2e+07 1.8e+09 -16500 0 31327

7.2e+07 1.8e+09 -16500 0 32011

7.2e+07 1.8e+09 -16500 0 32695

7.2e+07 1.8e+09 -16500 0 33379

7.2e+07 1.8e+09 -16500 0 34063

7.2e+07 1.8e+09 -16500 0 34747

7.2e+07 1.8e+09 -16500 0 35431

7.2e+07 1.8e+09 -16500 0 36114

7.2e+07 1.8e+09 -16500 0 36798

7.2e+07 1.8e+09 -16500 0 37482

7.2e+07 1.8e+09 -16500 0 38166

7.2e+07 1.8e+09 -16500 0 38960

7.2e+07 1.8e+09 -16500 -5e+05 5.4019e+05

7.2e+07 1.95e+09 -16500 -5e+05 5.4142e+05

7.2e+07 2.1e+09 -16500 -5e+05 5.4265e+05

7.2e+07 2.25e+09 -16500 -5e+05 5.4388e+05

7.2e+07 2.4e+09 -16500 -5e+05 5.4512e+05

7.2e+07 2.55e+09 -16500 -5e+05 5.4635e+05

7.2e+07 2.7e+09 -16500 -5e+05 5.4758e+05

7.2e+07 2.85e+09 -16500 -5e+05 5.8181e+05

7.2e+07 3e+09 -16500 -5e+05 5.8304e+05

7.2e+07 3.15e+09 -16500 -5e+05 5.8427e+05

7.2e+07 3.3e+09 -16500 -5e+05 5.855e+05

7.2e+07 3.45e+09 -16500 -5e+05 5.8677e+05

7.2e+07 3.6e+09 -16500 -5e+05 5.8815e+05

7.2e+07 3.75e+09 -16500 -5e+05 5.8953e+05

7.2e+07 3.9e+09 -16500 -5e+05 5.9091e+05

7.2e+07 4.05e+09 -16500 -5e+05 5.9229e+05

7.2e+07 4.2e+09 -16500 -5e+05 5.9367e+05

7.2e+07 4.35e+09 -16500 -5e+05 5.9505e+05

7.2e+07 4.5e+09 -16500 -5e+05 5.9643e+05

7.2e+07 4.65e+09 -16500 -5e+05 5.9781e+05

7.2e+07 4.8e+09 -16500 -5e+05 5.9919e+05

7.2e+07 4.95e+09 -16500 -5e+05 5.8408e+05

7.2e+07 5.1e+09 -16500 -5e+05 5.8546e+05

7.2e+07 5.25e+09 -16500 -5e+05 4.5447e+05

7.2e+07 5.4e+09 -16500 -5e+05 4.5612e+05

7.2e+07 5.55e+09 -16500 -5e+05 4.5777e+05

7.2e+07 5.7e+09 -16500 -5e+05 4.5942e+05

7.2e+07 5.85e+09 -16500 -5e+05 4.6107e+05

7.2e+07 6e+09 -16500 -5e+05 4.6272e+05

7.2e+07 6.15e+09 -16500 -5e+05 4.6437e+05

7.2e+07 6.3e+09 -16500 -5e+05 4.6602e+05

7.2e+07 6.45e+09 -16500 -5e+05 4.6767e+05

7.2e+07 6.6e+09 -16500 -5e+05 4.6932e+05

7.2e+07 6.75e+09 -16500 -5e+05 6.6897e+05

7.2e+07 6.9e+09 -16500 -5e+05 6.7062e+05

7.2e+07 7.05e+09 -16500 -5e+05 3.756e+05

7.2e+07 7.2e+09 -16500 -1.334e-08 -1.234e+05

7.2e+07 7.2e+09 -16500 0 -1.224e+05

7.2e+07 7.2e+09 -16500 0 -1.214e+05

7.2e+07 7.2e+09 -16500 0 -1.204e+05

7.2e+07 7.2e+09 -16500 0 -1.194e+05

7.2e+07 7.2e+09 -16500 0 2.1161e+05

7.2e+07 7.2e+09 -16500 0 2.1261e+05

7.2e+07 7.2e+09 -16500 0 2.1361e+05

7.2e+07 7.2e+09 -16500 0 2.1461e+05

7.2e+07 7.2e+09 -16500 0 2.1561e+05

7.2e+07 7.2e+09 -16500 0 2.1661e+05

7.2e+07 7.2e+09 -16500 0 -25221

7.2e+07 7.2e+09 -16500 0 -25239

7.2e+07 7.2e+09 -16500 0 -25258

7.2e+07 7.2e+09 -16500 0 -25276

7.2e+07 7.2e+09 -16500 0 -25294

7.2e+07 7.2e+09 -16500 0 40687

7.2e+07 7.2e+09 -16500 0 40669

7.2e+07 7.2e+09 -16500 0 40651

7.2e+07 7.2e+09 -16500 0 40632

7.2e+07 7.2e+09 -16500 0 40614

7.2e+07 7.2e+09 -16500 0 40596

7.2e+07 7.2e+09 -16500 0 40578

7.2e+07 7.2e+09 -16500 0 -98620

7.2e+07 7.2e+09 -16500 0 -99406

7.2e+07 7.2e+09 -16500 0 -1.0019e+05

7.2e+07 7.2e+09 -16500 0 -1.0098e+05

7.2e+07 7.2e+09 -16500 0 -1.0176e+05

7.2e+07 7.2e+09 -16500 0 -1.0255e+05

7.2e+07 7.2e+09 -16500 8.0039e-09 -1.0334e+05

7.2e+07 7.2e+09 -16500 5e+05 -6.0412e+05

7.2e+07 7.05e+09 -16500 5e+05 -6.0491e+05

7.2e+07 6.9e+09 -16500 5e+05 -6.0569e+05

7.2e+07 6.75e+09 -16500 5e+05 -6.0648e+05

7.2e+07 6.6e+09 -16500 5e+05 -6.0726e+05

7.2e+07 6.45e+09 -16500 5e+05 -6.8054e+05

7.2e+07 6.3e+09 -16500 5e+05 -6.7989e+05

7.2e+07 6.15e+09 -16500 5e+05 -6.7924e+05

7.2e+07 6e+09 -16500 5e+05 -6.7858e+05

7.2e+07 5.85e+09 -16500 5e+05 -6.7793e+05

7.2e+07 5.7e+09 -16500 5e+05 -6.7728e+05

7.2e+07 5.55e+09 -16500 5e+05 -6.7663e+05

7.2e+07 5.4e+09 -16500 5e+05 -6.7598e+05

7.2e+07 5.25e+09 -16500 5e+05 -6.7532e+05

7.2e+07 5.1e+09 -16500 5e+05 -6.7467e+05

7.2e+07 4.95e+09 -16500 5e+05 -6.7402e+05

7.2e+07 4.8e+09 -16500 5e+05 -6.7337e+05

7.2e+07 4.65e+09 -16500 5e+05 -6.6775e+05

7.2e+07 4.5e+09 -16500 5e+05 -6.6772e+05

7.2e+07 4.35e+09 -16500 5e+05 -6.677e+05

7.2e+07 4.2e+09 16500 5e+05 -1.9877e+06

7.2e+07 4.05e+09 16500 5e+05 -1.9876e+06

7.2e+07 3.9e+09 16500 5e+05 -1.9876e+06

7.2e+07 3.75e+09 16500 5e+05 -1.9876e+06

7.2e+07 3.6e+09 16500 5e+05 -1.9876e+06

7.2e+07 3.45e+09 16500 5e+05 -1.9875e+06

7.2e+07 3.3e+09 16500 5e+05 -1.9875e+06

7.2e+07 3.15e+09 16500 5e+05 -1.9875e+06

7.2e+07 3e+09 16500 5e+05 -1.9875e+06

7.2e+07 2.85e+09 16500 5e+05 -1.9605e+06

7.2e+07 2.7e+09 16500 5e+05 -1.961e+06

7.2e+07 2.55e+09 16500 5e+05 -1.9614e+06

7.2e+07 2.4e+09 16500 5e+05 -1.9619e+06

7.2e+07 2.25e+09 16500 5e+05 -1.9623e+06

7.2e+07 2.1e+09 16500 5e+05 -1.9628e+06

7.2e+07 1.95e+09 16500 5e+05 -1.9633e+06

7.2e+07 1.8e+09 16500 0 -1.4637e+06

7.2e+07 1.8e+09 16500 0 -1.4807e+06

7.2e+07 1.8e+09 16500 0 -1.4811e+06

7.2e+07 1.8e+09 16500 0 -1.4816e+06

7.2e+07 1.8e+09 16500 0 -1.4821e+06

7.2e+07 1.8e+09 16500 0 -1.3138e+06

7.2e+07 1.8e+09 16500 0 -1.3143e+06

7.2e+07 1.8e+09 16500 0 -1.2489e+06

7.2e+07 1.8e+09 16500 0 -1.2494e+06

7.2e+07 1.8e+09 16500 0 -1.25e+06

7.2e+07 1.8e+09 16500 0 -1.2505e+06

7.2e+07 1.8e+09 16500 0 -1.251e+06

7.2e+07 1.8e+09 16500 0 -1.2516e+06

7.2e+07 1.8e+09 16500 0 -1.2521e+06

7.2e+07 1.8e+09 16500 0 -1.2526e+06

7.2e+07 1.8e+09 16500 0 -1.2532e+06

7.2e+07 1.8e+09 16500 0 -1.2537e+06

7.2e+07 1.8e+09 16500 0 -8.4094e+05

7.2e+07 1.8e+09 16500 0 -8.4202e+05

7.2e+07 1.8e+09 16500 0 -8.431e+05

7.2e+07 1.8e+09 16500 0 -8.4418e+05

7.2e+07 1.8e+09 16500 0 -8.4526e+05

7.2e+07 1.8e+09 16500 0 -8.4634e+05

7.2e+07 1.8e+09 16500 0 -8.4742e+05

7.2e+07 1.8e+09 16500 0 -8.485e+05

7.2e+07 1.8e+09 16500 0 -8.4959e+05

7.2e+07 1.8e+09 16500 -1.0545e-09 -8.5067e+05

7.2e+07 1.8e+09 16500 1.0949e-09 -8.5175e+05

7.2e+07 1.8e+09 -16500 1.2775e-09 -77328

7.2e+07 1.8e+09 -16500 -1.3262e-09 1.7958e+05

7.2e+07 1.8e+09 -16500 0 1.7854e+05

7.2e+07 1.8e+09 -16500 0 1.7749e+05

7.2e+07 1.8e+09 -16500 0 1.7644e+05

7.2e+07 1.8e+09 -16500 0 1.7539e+05

7.2e+07 1.8e+09 -16500 -2.1344e-08 1.7435e+05

7.2e+07 1.8e+09 -16500 -5e+05 6.733e+05

7.2e+07 1.95e+09 -16500 -5e+05 6.7225e+05

7.2e+07 2.1e+09 -16500 -5e+05 6.7121e+05

7.2e+07 2.25e+09 -16500 -5e+05 6.7016e+05

7.2e+07 2.4e+09 -16500 -5e+05 6.6911e+05

7.2e+07 2.55e+09 -16500 -5e+05 6.6807e+05

7.2e+07 2.7e+09 -16500 -5e+05 8.1026e+05

7.2e+07 2.85e+09 -16500 -5e+05 8.0963e+05

7.2e+07 3e+09 -16500 -5e+05 8.0901e+05

7.2e+07 3.15e+09 -16500 -5e+05 8.0839e+05

7.2e+07 3.3e+09 -16500 -5e+05 8.0776e+05

7.2e+07 3.45e+09 -16500 -5e+05 8.0714e+05

7.2e+07 3.6e+09 -16500 -5e+05 8.0651e+05

7.2e+07 3.75e+09 -16500 -5e+05 8.0589e+05

7.2e+07 3.9e+09 -16500 -5e+05 8.0527e+05

7.2e+07 4.05e+09 -16500 -5e+05 8.0464e+05

7.2e+07 4.2e+09 -16500 -5e+05 8.0402e+05

7.2e+07 4.35e+09 -16500 -5e+05 8.034e+05

7.2e+07 4.5e+09 -16500 -5e+05 7.0381e+05

7.2e+07 4.65e+09 -16500 -5e+05 7.0336e+05

7.2e+07 4.8e+09 -16500 -5e+05 7.0292e+05

7.2e+07 4.95e+09 -16500 -5e+05 7.0247e+05

7.2e+07 5.1e+09 -16500 -5e+05 7.0202e+05

7.2e+07 5.25e+09 -16500 -5e+05 7.0158e+05

7.2e+07 5.4e+09 -16500 -5e+05 7.0113e+05

7.2e+07 5.55e+09 -16500 -5e+05 7.0068e+05

7.2e+07 5.7e+09 -16500 -5e+05 7.0024e+05

7.2e+07 5.85e+09 -16500 -5e+05 6.9979e+05

7.2e+07 6e+09 -16500 -5e+05 6.9935e+05

7.2e+07 6.15e+09 -16500 -5e+05 6.494e+05

7.2e+07 6.3e+09 -16500 -5e+05 6.4896e+05

7.2e+07 6.45e+09 -16500 -5e+05 6.4854e+05

7.2e+07 6.6e+09 -16500 -5e+05 6.4812e+05

7.2e+07 6.75e+09 -16500 -5e+05 6.477e+05

7.2e+07 6.9e+09 16500 -5e+05 4.4928e+05

7.2e+07 7.05e+09 16500 -5e+05 4.4886e+05

7.2e+07 7.2e+09 16500 0 -51554

7.2e+07 7.2e+09 16500 0 -51972

7.2e+07 7.2e+09 -16500 0 46609

2.88e+08 7.2e+09 -16500 -5e+05 5.4619e+05

[NN]

Is it possible to get a signle value or a final value for optimised objective function and the variables instead of all these 241*5 table ?

kindly advice.

[Alan Weiss]

It is clear that solve is using intlinprog as the underlying solver. You have a mixed-integer linear programming problem.

The solution sol and objective function value fval are returned in

[sol,fval] = solve(prob,'Options',opts)

If that does not answer your question, then I obviously didn't understand what you were asking. Maybe ask in a different way, with an example of what you want.

Alan Weiss

MATLAB mathematical toolbox documentation

[NN]

Thank you very much. I understood it now, but i have a question, Please be kind to clear my doubt.I am getting Favl value very big,

I have tried to plot the values of optimal solution (minimal cost in this problem)

When I used the command

options = optimoptions(prob.optimoptions,'Display','iter');

I am getting a big value of 8.97e10 $/hr as Fval(optimal solution).

Please check this and confirm if it is correct or not or since the problem using an optimized time vector(dt=300), Is it required to modify the fval we get at the end (8.97e10 $/hr.)?

Please let me know. I am attaching the modified files(battsolaropt) and the diagnostic viewer screen for your reference. It is written No feasible solution was found.

Also please let me know why a gain of (3600*100) is used for calculating cost in Simulink, Why 100 is there?

Please advice .This has been posted as a seperate post here also,

https://www.mathworks.com/matlabcentral/answers/1456834-question-about-finding-the-optimal-value-_problem-based-optimization

[NN]

as per the problem,only 241 x1 table will be the answer for each variable or single variable i can get ?

[Alan Weiss]

I'm sorry, but I have reached the limit of the time I am willing to put in investigating your problem.

Alan Weiss

MATLAB mathematical toolbox documentation

[NN]

Ok .That is really sad.

Thank you very much for your time and consideration