MaxIterations is an option that specifies the maximum number of iterations that should be tried in various built-in functions and algorithms.
FindMaximum[f, x] searches for a local maximum in f, starting from an automatically selected point.FindMaximum[f, {x, x_0}] searches for a local maximum in f, starting from ...
Constrained optimization problems are problems for which a function f(x) is to be minimized or maximized subject to constraints Φ(x). Here f:^n is called the objective ...
Maximize[f, x] maximizes f with respect to x.Maximize[f, {x, y, ...}] maximizes f with respect to x, y, .... Maximize[{f, cons}, {x, y, ...}] maximizes f subject to the ...
Numerical algorithms for constrained nonlinear optimization can be broadly categorized into gradient-based methods and direct search methods. Gradient-based methods use first ...
FindArgMax[f, x] gives the position x_max of a local maximum of f.FindArgMax[f, {x, x_0}] gives the position x_max of a local maximum of f, found by a search starting from ...
FindMaxValue[f, x] gives the value at a local maximum of f.FindMaxValue[f, {x, x_0}] gives the value at a local maximum of f, found by a search starting from the point x = ...
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, ...}] ...
DavisDistribution[b, n, \[Mu]] represents a Davis distribution with scale parameter b, shape parameter n, and location parameter \[Mu].
x >= y or x >= y yields True if x is determined to be greater than or equal to y. x_1 >= x_2 >= x_3 yields True if the x_i form a non-increasing sequence.