Package minfx

This is the minfx optimisation library.

Version: trunk

Submodules

• minfx.base_classes: Bases classes for the minfx optimisation library.
• minfx.bfgs: Quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) nonlinear optimization.
• minfx.cauchy_point: Cauchy Point trust region nonlinear optimization.
• minfx.constraint_linear: Linear inequality constraint functions and gradients.
• minfx.coordinate_descent: Back-and-forth coordinate descent (CD) optimization.
• minfx.dogleg: Dogleg trust region optimization.
• minfx.errors: Module containing all of the MinfxError objects.
• minfx.exact_trust_region: Exact trust region optimization.
• minfx.fletcher_reeves_cg: Fletcher-Reeves conjugate gradient optimization.
• minfx.generic: Generic minimisation function for easy access to all of the optimization algorithms.
• minfx.grid: The grid search algorithm.
• minfx.hessian_mods: The Hessian modification package.
  • minfx.hessian_mods.cholesky_mod: Cholesky Hessian modification with added multiple of the identity.
  • minfx.hessian_mods.eigenvalue: Eigenvalue Hessian modification.
  • minfx.hessian_mods.gmw81: The Gill, Murray, and Wright (GMW) modified Cholesky Hessian modification algorithm.
  • minfx.hessian_mods.gmw81_old: The Gill, Murray, and Wright (GMW) modified Cholesky Hessian modification algorithm.
  • minfx.hessian_mods.se99: The Schnabel and Eskow (SE) revised modified Cholesky factorisation Hessian modification algorithm.
• minfx.hestenes_stiefel_cg: Hestenes-Stiefel conjugate gradient optimization.
• minfx.levenberg_marquardt: Levenberg-Marquardt optimization.
• minfx.line_search: The line search algorithm package.
  • minfx.line_search.backtrack: Backtracking line search algorithm.
  • minfx.line_search.interpolate: Quadratic and cubic interpolation method.
  • minfx.line_search.more_thuente: A line search algorithm from More and Thuente.
  • minfx.line_search.nocedal_wright_interpol: A line search algorithm based on interpolation.
  • minfx.line_search.nocedal_wright_wolfe: A line search algorithm implemented using the strong Wolfe conditions.
  • minfx.line_search.test_functions: Testing functions.
• minfx.log_barrier_function: Logarithmic barrier function optimization constraint algorithm.
• minfx.method_of_multipliers: Method of multipliers or augmented Lagrangian method optimization constraint algorithm.
• minfx.ncg: Line search Newton conjugate gradient optimization.
• minfx.newton: Newton (or Newton-Raphson - NR) optimization.
• minfx.polak_ribiere_cg: Polak-Ribiere conjugate gradient optimization.
• minfx.polak_ribiere_plus_cg: Polak-Ribiere + conjugate gradient optimization.
• minfx.simplex: Downhill simplex optimization.
• minfx.steepest_descent: Steepest descent (SD) optimization.
• minfx.steihaug_cg: Steihaug conjugate-gradient trust region optimization.

Variables

__package__ = None
hash(x)

Imports: errors, hessian_mods, line_search, scipy_subset