Specifying Derivatives (Mathematica Tutorial)

The function FindRoot has a Jacobian option; the functions FindMinimum, FindMaximum, and FindFit have a Gradient option; and the "Newton" method has a method option Hessian. ...

PairedHistogram (Built-in Mathematica Symbol)

PairedHistogram[{x_1, x_2, ...}, {y_1, y_2, ...}] plots a paired histogram of the values x_i and y_i.PairedHistogram[{x_1, x_2, ...}, {y_1, y_2, ...}, bspec] plots a paired ...

CylindricalDecomposition (Built-in Mathematica Symbol)

CylindricalDecomposition[ineqs, {x_1, x_2, ...}] finds a decomposition of the region represented by the inequalities ineqs into cylindrical parts whose directions correspond ...

Parallel Computation (Compiled Function Tools Tutorial)

The Mathematica compiler can run computations in parallel. It does this by threading a compiled function over lists of data in parallel. A first step is to create a compiled ...

ND (Numerical Calculus Package Symbol)

ND[expr, x, x_0] gives a numerical approximation to the derivative of expr with respect to x at the point x_0.ND[expr, {x, n}, x_0] gives a numerical approximation to the ...

NSolve (Built-in Mathematica Symbol)

NSolve[expr, vars] attempts to find numerical approximations to the solutions of the system expr of equations or inequalities for the variables vars. NSolve[expr, vars, ...

Resolve (Built-in Mathematica Symbol)

Resolve[expr] attempts to resolve expr into a form that eliminates ForAll and Exists quantifiers. Resolve[expr, dom] works over the domain dom. Common choices of dom are ...

SystemsModelFeedbackConnect (Built-in Mathematica Symbol)

SystemsModelFeedbackConnect[sys] gives the closed-loop system for the StateSpaceModel or TransferFunctionModel object sys with unity negative feedback. ...

Linear Programming (Mathematica Tutorial)

Linear programming problems are optimization problems where the objective function and constraints are all linear. Mathematica has a collection of algorithms for solving ...

Random Number Generation (Mathematica Tutorial)

The ability to generate pseudorandom numbers is important for simulating events, estimating probabilities and other quantities, making randomized assignments or selections, ...