The CoordinateDescent class solves unconstrained Non-Linear Programming (NLP) problems using the Coordinate Descent algorithm. Given a function f and a starting point x0, the algorithm picks coordinate directions (cyclically) and takes steps in the those directions. The algorithm iterates until it converges.
dir_k = kth coordinate direction
min f(x)
Value parameters
exactLS
whether to use exact (e.g., GoldenLS) or inexact (e.g., WolfeLS) Line Search
The objective function f plus a weighted penalty based on the constraint function g. Override for constrained optimization and ignore for unconstrained optimization.
The objective function f plus a weighted penalty based on the constraint function g. Override for constrained optimization and ignore for unconstrained optimization.
Solve the following Non-Linear Programming (NLP) problem: min { f(x) | g(x) <= 0 }. To use explicit functions for gradient, replace gradient (fg, x._1 + s) with gradientD (df, x._1 + s). This method uses multiple random restarts.
Solve the following Non-Linear Programming (NLP) problem: min { f(x) | g(x) <= 0 }. To use explicit functions for gradient, replace gradient (fg, x._1 + s) with gradientD (df, x._1 + s). This method uses multiple random restarts.