GeneralizedLinearModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a generalized linear model of the form g -1 (\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + ...) that ...

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. ...

FindDistributionParameters[data, dist] finds the parameter estimates for the distribution dist from data.FindDistributionParameters[data, dist, {{p, p_0}, {q, q_0}, ...}] ...

Mathematica has a collection of commands that do unconstrained optimization (FindMinimum and FindMaximum) and solve nonlinear equations (FindRoot) and nonlinear fitting ...

A method like "Newton's" method chooses a step, but the validity of that step only goes as far as the Newton quadratic model for the function really reflects the function. ...

Mathematically, sufficient conditions for a local minimum of a smooth function are quite straightforward: x^* is a local minimum if ∇f(x^*)=0 and the Hessian ∇^2f(x^*) is ...

Mathematica has over 3000 built-in functions and other objects, all based on a single unified framework, and all carefully designed to work together, both in simple ...

General issues about the internal implementation of Mathematica are discussed in "The Internals of Mathematica". Given here are brief notes on particular features. These ...

The shooting method works by considering the boundary conditions as a multivariate function of initial conditions at some point, reducing the boundary value problem to ...

PolyhedronData[poly, " property"] gives the value of the specified property for the polyhedron named poly.PolyhedronData[poly] gives an image of the polyhedron named ...