- Fortran code for general nonlinear
programming that does not use matrix manipulations at all
and, so, is able to solve extremely large problems with
moderate computer time. The general algorithm is of
Augmented Lagrangian type and the subproblems are solved
using GENCAN. GENCAN (included in ALGENCAN) is a Fortran
code for minimizing a smooth function with a potentially
large number of variables and box-constraints.
- Fortran code for minimizing a smooth function with
convex constraints with a potentially very large number of
- OTHER METHODS
- In addition to the methods above, there are also other
methods that use ALGENCAN and/or SPG as subalgorithms.
Those other methods are not being updated. So, for the
newest versions of ALGENCAN and/or SPG follow the links
above. The available methods are:
ALABB: Augmented Lagrangian for Global Optimization
that uses the αBB method to find the global
solution of linearly constraint subproblems.
ALBETRA: Augmented Lagrangian that uses BETRA
to solve bound-constrained subproblems. BETRA is an active
set method for bound constraint minimization that uses the
classical Euclidian trust-region method inside the faces.
SPG directions are used to abandon faces.
ALGENCAN-NEWTON: This method is an attempt to improve
the local convergence of ALGENCAN. The Newton′s method
is used to solve a KKT system identified by the Augmented
ALSPG: Augmented Lagrangian that uses the SPG method
to solve convex constrained subproblems.
IVM: Inexact Variable Metric method for convex
SCG: Spectral Conjugate Gradient method for
GENLIN: Partial Spectral Projected Gradient Method
with Active-Set Strategy for Linearly Constrained
ALGENCAN-OTR: Outer Trust-Region method for Constrained
- C code for the estimation of the thickness and the
optical constants of thin films. It is based on unconstrained
minimization and uses the Spectral Gradient method.
- Fortran code to create initial configurations for
molecular dynamics. The problem is modeled as a bound-constrained
minimization problem and GENCAN is used to solve it.
- CUTTING AND PACKING
- Large variety of nonlinear models for packing problems.