![]() But you really need to provide more details. advectionpde, a MATLAB code which solves the advection partial differential equation (PDE) dudt + c dudx 0 in one spatial dimension, with a constant velocity c, and periodic boundary conditions, using the FTCS method, forward time difference, centered space difference. Optimization Toolbox provides functions for finding parameters that minimize or maximize objectives while satisfying constraints. Are you able to provide the jacobian of your objective function? If you can, I would probably experiment first with a multi-start Levenberg-Marquardt, I have had good results with this approach. ![]() One of the most versatile is fmincon, a function minimizer with linear and nonlinear constraints. ![]() Since you haven’t said anything about your problem it’s difficult to give a meaningful suggestion. The optimize toolbox in MATLAB has linear and nonlinear solvers. Solving different control problem using YALMIP: We take an example related to control problem and solve using YALMIP. With the help of double (x) command nd the optimal solution. You can override the default by using the 'solver' name-value pair argument when calling solve. LMI and YALMIP: Modeling and Optimization Toolbox in MATLAB 511 YALMIP shall automatically divide as a linear programming problem and take appropriate solver. For the default solver for the problem and supported solvers for the problem, see the solvers function. However, depending on the size (number of variables) or your problem, you may not be able to get any better answer: for large problems, derivative-free algorithms are basically limited unless your objective function is extremely fast to evaluate and you’re allowing millions of function evaluations. Internally, the solve function solves optimization problems by calling a solver. On the other hand, if the fit is very difficult and you’re coming up with ridiculous results, then you may have to resort to global solvers. When you do your curve-fitting using standard least-squares approaches (I.e., Levenberg-Marquardt, Gauss-Newton, etc.), is your fit acceptable? If the answer is “yes”, then there’s no point in using global optimization algorithms.
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