NMaximize[f, x] maximizes f numerically with respect to x. NMaximize[f, {x, y, ...}] maximizes f numerically with respect to x, y, .... NMaximize[{f, cons}, {x, y, ...}] ...

One of the important features of Mathematica is its ability to give you exact, symbolic, results for computations. There are, however, computations where it is just ...

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 ...

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

NArgMax[f, x] gives a position x_max at which f is numerically maximized. NArgMax[f, {x, y, ...}] gives a position {x_max, y_max, ...} at which f is numerically maximized. ...

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

ProcessEstimator is an option to EstimatedProcess and FindProcessParameters that specifies what process parameter estimator to use.

ParameterEstimator is an option to EstimatedDistribution and FindDistributionParameters that specifies what parameter estimator to use.

Mathematica's handling of polynomial systems is a tour de force of algebraic computation. Building on mathematical results spanning more than a century, Mathematica for the ...

Integrated into Mathematica is a full range of state-of-the-art local and global optimization techniques, both numeric and symbolic, including constrained nonlinear ...