how to speed up a nonlinear convex problem if solved using fmincon
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I have a nonlinear convex problem with a large number of variables (100*n_samples+66). For example, if n_samples=50, I have 5066 variables. I have a set of linear constraints and a set of nonlinear constraints. The nonlinear constraints are convex and the objective function is a linear function that has 66 variables. To get a solution, the optimizer terminated because it reached the maximum number of function evaluations which I set at 90000 (it took 13 hours for the optimizer to stop). This is a smaple of the iteration when n_samples=1, i.e., we have 166 variables. Any idea how to speed up the optimizer (i have matlab 2013a ).
First-order Norm of
Iter F-count f(x) Feasibility optimality step
871 149095 5.800103e+03 1.776e-15 1.469e+00 5.745e-01
872 149269 5.798809e+03 1.776e-15 1.444e+00 5.761e-01
873 149443 5.797519e+03 1.776e-15 1.444e+00 5.774e-01
874 149617 5.796233e+03 1.849e-15 1.444e+00 5.785e-01
875 149791 5.794949e+03 4.441e-16 1.444e+00 5.793e-01
876 149965 5.793668e+03 4.031e-15 1.444e+00 5.799e-01
877 150139 5.792388e+03 3.553e-15 1.444e+00 5.802e-01
878 150311 5.789831e+03 1.776e-15 1.444e+00 1.161e+00
879 150483 5.787275e+03 6.314e-16 1.444e+00 1.160e+00
880 150655 5.782154e+03 1.776e-15 1.444e+00 2.315e+00
881 150827 5.776978e+03 1.776e-15 1.444e+00 2.297e+00
882 151005 5.772331e+03 1.776e-15 1.316e+00 1.987e+00
883 151181 5.769920e+03 1.776e-15 1.136e+00 9.909e-01
884 151357 5.768691e+03 2.429e-15 1.053e+00 4.984e-01
885 151531 5.767452e+03 2.984e-15 9.999e-01 5.006e-01
886 151705 5.766207e+03 1.776e-15 9.999e-01 5.029e-01
887 151879 5.764957e+03 1.776e-15 9.999e-01 5.051e-01
888 152051 5.763706e+03 1.776e-15 9.999e-01 5.071e-01
889 152223 5.762454e+03 1.776e-15 9.999e-01 5.090e-01
890 152395 5.761203e+03 3.372e-15 9.999e-01 5.108e-01
891 152567 5.759954e+03 1.776e-15 9.999e-01 5.125e-01
892 152739 5.758708e+03 1.776e-15 9.999e-01 5.140e-01
893 152911 5.757463e+03 3.497e-15 9.999e-01 5.155e-01
894 153085 5.756221e+03 1.776e-15 9.999e-01 5.168e-01
895 153259 5.754981e+03 8.812e-16 9.999e-01 5.179e-01
896 153433 5.753743e+03 1.776e-15 9.999e-01 5.189e-01
1 comentario
Piotr Balik
el 5 de Abr. de 2023
Bumping the question, I have similar problem - convexity assumption should give a great boost
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