fmincon estimates gradients of objective and nonlinear constraint functions by taking finite differences. A finite difference calculation stepped outside the region where a function is well-defined, returning Inf, …

Constrained Nonlinear Optimization Algorithms Constrained Optimization Definition Constrained minimization is the problem of finding a vector x that is a local minimum to a scalar function f (x) …

Nonlinear Constraints Several optimization solvers accept nonlinear constraints, including fmincon, fseminf, fgoalattain, fminimax, and the Global Optimization Toolbox solvers ga (Global Optimization …

This tutorial includes multiple examples that show how to use two nonlinear optimization solvers, fminunc and fmincon, and how to set options. The principles outlined in this tutorial apply to the other …

The fmincon interior-point algorithm can accept a Hessian function as an input. When you supply a Hessian, you can obtain a faster, more accurate solution to a constrained minimization problem. The …

The algorithm can use sparse data; see Sparsity in Optimization Algorithms. The algorithm can use special techniques to save memory usage, such as a Hessian multiply function. For details, see …

Nov 25, 2024 · "fmincon" is designed to minimize the objective function. If your objective function returns negative values (for example, objective = -1), "fmincon" will indeed try to make it more negative, …

Additionally, to make the history available outside of the fmincon function, perform the optimization inside a nested function that calls fmincon and returns the output function variables. For more …

Compare the performance of lsqnonlin and fmincon on a nonlinear least-squares problem with nonlinear constraints.

Create Nondefault Options Set options for fmincon to use the sqp algorithm and at most 1500 iterations.