Introduction Methods for Local Minimization Methods for Solving Nonlinear Equations
Expectation[expr, x \[Distributed] dist] gives the expectation of expr under the assumption that x follows the probability distribution dist. Expectation[expr, x ...
ErrorBarFunction is an option for ErrorListPlot that specifies a function to apply to determine the shape of error bars.
CopulaDistribution[ker, {dist_1, dist_2, ...}] represents a copula distribution with kernel distribution ker and marginal distributions dist_1, dist_2, ....
All the test problems presented in [MGH81] have been coded into Mathematica in the Optimization`UnconstrainedProblems` package. A data structure is used so that the problems ...
Exists
(Built-in Mathematica Symbol) Exists[x, expr] represents the statement that there exists a value of x for which expr is True. Exists[x, cond, expr] states that there exists an x satisfying the condition ...
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 ...
FindArgMin[f, x] gives the position x_min of a local minimum of f.FindArgMin[f, {x, x_0}] gives the position x_min of a local minimum 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 = ...
FindMinValue[f, x] gives the value at a local minimum of f.FindMinValue[f, {x, x_0}] gives the value at a local minimum of f, found by a search starting from the point x = ...