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Open Directory - Computers: Programming: Languages: Fortran: Source Code: Optimization
Science: Math: Combinatorics: Software: Linear Programming and Optimization
Science: Math: Operations Research
- Fortran 90 package for finding approximate solutions of certain binary quadratic programs, currently including the Max-Cut and the Max-Bisection problems.
Constrained and Unconstrained Testing Environment, revisted (CUTEr)
- Testing environment for optimization and linear algebra solvers. The package includes a collection of test problems, along with Fortran 77, Fortran 90/95 and Matlab tools intended to help developers design, compare and improve new and existing solvers.
- Fortran 77 code for general differentiable nonlinear programming using dense linear algebra, by Peter Spellucci.
Decision Tree for Optimization Software
- Linear and non-linear optimization, constrained and unconstrained. Mainly Fortran 77 but some in C.
- Fortran 90 code implementing the Differential Evolution method, a simple but powerful population-based, stochastic function minimizer.
Fortran 95 Codes for Convex Network Optimization
- Modules for random number generation, plotting, sorting, ranking, combinatorics, network optimization, and optimization. Written in FWEB by Aleksandar Donev.
Fortran Genetic Algorithm
- Initializes a random sample of individuals with different parameters to be optimized using the genetic algorithm approach -- evolution via survival of the fittest. By David Carroll.
- Thread-safe library of Fortran 90 packages for solving nonlinear optimization problems, covering unconstrained and bound-constrained optimization, quadratic programming, nonlinear programming, systems of nonlinear equations and inequalities, and nonlinear least squares problems.
- Links to global optimization code, mainly in Fortran 77 and C, but some links to local minimizers as well.
Klaus Schittkowski's Optimization Software
- Nonlinear programming codes: SQP, least squares, multicriteria optimization, data fitting in ODEs, DAEs, PDEs, PDAEs.
Ladislav Luksan: Subroutines
- Fortran 77 codes for nondifferentiable, large-scale, or dense nonlinear optimization. Mostly under the GNU General Public License.
- Fortran 77 package for solving unconstrained and constrained optimization problems, systems of nonlinear equations, and nonlinear least-squares problems.
- Ratfor code for the primal-dual log barrier form of the interior point LP solver of Lustig, Marsten and Shanno, ORSA J Opt 1992.
- Fortran 77 code for solving nonlinear equations and nonlinear least squares problems. Five algorithmic paths each include a core subroutine and an easy-to-use driver. The algorithms proceed either from an analytic specification of the Jacobian matrix or directly from the problem functions.
- Optimization codes at Netlib.
Optimization Software Developed by J. Nocedal's Research Team
- KNITRO, General Nonlinear Programming Solver; L-BFGS, Limited Memory Codes; PREQN, Preconditioning the Conjugate Gradient Method; and CG+, Conjugate Gradient Software.
Projection-Type Methods for Large Quadratic Programs
- Fortran 77 code by Valentia Ruggiero for large and sparse convex quadratic programs.
Quadratic Assignment Problem Library (QAPLIB)
- By R.E. Burkard, E. Çela, S.E. Karisch and F. Rendl.
- Subspace-searching simplex method for the unconstrained optimization of general multivariate functions, generalizing the Nelder-Mead simplex method.
Stanford Business Software
- Sells Fortran 77 optimization codes MINOS (linear programming and nonlinear optimization), SNOPT (large-scale quadratic and nonlinear programming), NPSOL (nonlinear programming), LSSOL (Linearly constrained linear least squares problems and convex quadratic programming), and QPOPT (linear and quadratic programming).
TANGO: Trustable Algorithms for Nonlinear General Optimization
- A set of Fortran routines for optimization developed at the University of São Paulo. Includes downloads, test problems, and related publications.
- Trust region Newton method for the solution of large bound-constrained optimization problems, by Chih-Jen Lin and Jorge Moré.
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