The LBFGS_B the class implements the Limited memory Broyden–Fletcher– Goldfarb–Shanno for Bounds constrained optimization (L-BFGS-B) Quasi-Newton Algorithm for solving Non-Linear Programming (NLP) problems. L-BFGS-B determines a search direction by deflecting the steepest descent direction vector (opposite the gradient) by * multiplying it by a matrix that approximates the inverse Hessian. Furthermore, only a few vectors represent the approximation of the Hessian Matrix (limited memory). The parameters estimated are also bounded within user specified lower and upper bounds.
Perform a line search from point x along direction dir, returning the accepted step length. This method satisfies the Minimizer trait contract. For exact scalar search requests it delegates to lineSearch1D; otherwise it delegates to the native More-Thuente bounded line search and returns only the accepted step length.
Perform a line search from point x along direction dir, returning the accepted step length. This method satisfies the Minimizer trait contract. For exact scalar search requests it delegates to lineSearch1D; otherwise it delegates to the native More-Thuente bounded line search and returns only the accepted step length.
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.