NMinimize[f, x] minimizes f numerically with respect to x.NMinimize[f, {x, y, ...}] minimizes f numerically with respect to x, y, .... NMinimize[{f, cons}, {x, y, ...}] ...

Searching for local minima and maxima. This finds the value of x which minimizes Γ(x), starting at x2. The last element of the list gives the value at which the minimum is ...

FindFit[data, expr, pars, vars] finds numerical values of the parameters pars that make expr give a best fit to data as a function of vars. The data can have the form {{x_1, ...

An important subset of optimization problems is constrained nonlinear optimization, where the function is not linear and the parameter values are constrained to certain ...

NMinValue[f, x] gives the minimum value of f with respect to x.NMinValue[f, {x, y, ...}] gives the minimum value of f with respect to x, y, .... NMinValue[{f, cons}, {x, y, ...

NMinimize, NMaximize, Minimize, and Maximize employ global optimization algorithms, and are thus suitable when a global optimum is needed. Minimize and Maximize can find ...

NArgMin[f, x] gives a position x_min at which f is numerically minimized.NArgMin[f, {x, y, ...}] gives a position {x_min, y_min, ...} at which f is numerically ...

Here is one way to get multiple minima: call NMinimize multiple times with different random seeds, which will cause different optimization paths to be taken. This defines a ...

Mathematica uses a large number of original algorithms to provide automatic systemwide support for inequalities and inequality constraints. Whereas equations can often be ...

Numerical algorithms for constrained nonlinear optimization can be broadly categorized into gradient-based methods and direct search methods. Gradient-based methods use first ...