Name of the Package | Author(s) | Purpose |
---|---|---|
CONMAX | Edwin H. Kaufman Jr.
David J. Leeming Gerald D. Taylor | CONMAX uses ODE's to generate search direction followed by Newton method to correct back to the feasible region.
References: E. H. Kaufman, Jr., D. J. Leeming, G. D. Taylor, An ODE-based approach to nonlinearly constrained MINIMAX problems, Numerical Algorithms (1995), pp. 25-37. |
FSQP | Eliane R. Panier
University of Maryland, College Park Andre Tits University of Maryland, College Park E-mail: andre@isr.umd.edu Craig Lawrence E-mail: craigl@isr.umd.edu HISTORY | Portable implementations (in both C and Fortran) of the Feasible Sequential Quadratic Programming (FSQP) algorithm.
A superlinearly convergent algorithm for directly tackling optimization problems with: Multiple competing linear/nonlinear objective functions (minimax). Linear/nonlinear inequality constraints. Linear/nonlinear equality constraints. C. T. Lawrence, J. L. Zhou and A. L. Tits, User's Guide for CFSQP Version 2.5: A C Code for Solving (Large Scale) Constrained Nonlinear (Minimax) Optimization Problems, Generating Iterates Satisfying All Inequality Constraints, Institute for Systems Research, University of Maryland, Technical Report TR-94-16r1, College Park, MD 20742, 1997. Postscript file J. L. Zhou, A. L. Tits and C. T. Lawrence, User's Guide for FFSQP Version 3.7 : A Fortran Code for Solving Optimization Programs, Possibly Minimax, with General Inequality Constraints and Linear Equality Constraints, Generating Feasible Iterates, Institute for Systems Research, University of Maryland,Technical Report SRC-TR-92-107r5, College Park, MD 20742, 1997. Postscript file |
MCMA | Janusz Granat
Marek Makowski IIASA, Schlossplatz 1 A-2361 Laxenburg, Austria | MCMA (Multiple-Criteria Model Analysis) is a tool for multiple criteria model analysis.
MCMA can be used for analysis of any LP (including MIP) model that can be provided in the LP_DIT format (models available in the MPS format can be easily converted by the LPDIT program to the LP_DIT format). In order to use MCMA you will need the following software: HOPDM, MOMIP, only if your model contains integer variables, LPDIT, only if you would like to use MCMA for analysis of your own model, tutorial core models, only, if you want to run the tutorial examples for MCMA. |
NBI | Indraneel Das
Department of Computational and Applied Mathematics, Rice University. E-mail: indraneel_das@email.mobil.com John E. Dennis, Jr. Department of Computational and Applied Mathematics, Rice University. E-mail: dennis@caam.rice.edu | Normal-Boundary Intersection (NBI), is a new technique for solving nonlinear multicriteria optimization problems.
Matlab implementation. References: Indraneel Das and John E. Dennis, Jr. Normal-Boundary Intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems" published in SIAM J. Optimization 8(1998), pp 631-657. |
NIMBUS | Kaisa Miettinen
University of Jyväskylä, Department of Mathematical Information Technology E-mail: miettine@mit.jyu.fi Marko M. Mäkelä University of Jyväskylä, Department of Mathematical Information Technology E-mail: makela@mit.jyu.fi | NIMBUS is a Nondifferentiable Interactive Multiobjective Bundle-based optimization System.
NIMBUS is suitable for both differentiable and nondifferentiable multiobjective and single objective optimization problems subject to nonlinear and linear constraints with bounds for the variables. NIMBUS operates via the Internet. In other words, the user does not need to load any software to her or his own computer but all the calculation, input of the problem etc. takes place via WWW. The system can be used from any type of computer connected to the Internet that has a WWW browser. References: - Miettinen K., Mäkelä M.M., Männikkö T. Nondifferentiable Multiobjective Optimizer NIMBUS Applied to an Optimal Control Problem of Continuous Casting, Report 22/1996, University of Jyväskylä, Department of Mathematics, Laboratory of Scientific Computing, 1996. - Miettinen K., Mäkelä M.M. Comparing Two Versions of NIMBUS Optimization System, Report 23/1996, University of Jyväskylä, Department of Mathematics, Laboratory of Scientific Computing, 1996. - Miettinen K., Mäkelä M.M. Optimization System WWW-NIMBUS, Report 9/1998, University of Jyväskylä, Department of Mathematics, Laboratory of Scientific Computing, Jyväskylä, 1998 |
NLPJOB | Klaus Schittkowski
University of Bayreuth Department of Mathematics D-95440 Bayreuth, Germany e-mail: klaus.schittkowski@uni-bayreuth.de | NLPJOB solves multicriteria problems interactively, i.e. problems with more than one objective function.
By using a suitable transformation, a scalar nonlinear problem is created and solved by NLPQL (Klaus Schittkowski). There are 15 different options available for formulating the scalar subproblem. Weights, bounds and type of the scalar objective function can be changed interactively. Lists of efficient points and corresponding objective function values are generated and stored on files. Special Features: numerical differentiation included, interactive help option, full documentation by initial comments, FORTRAN source code |
PROTASS | Rafal Cytrycki
E-mail: rcytrycki@computerland.pl Andrzej Dzik E-mail: Andrzej.Dzik@ekspert.szczecin.pl | Linear multiobjective problems.
Current version of PROTASS supports two methods: STEM and HSJ. Algorithms used in Simplex computations are based on lp_solve 2.0. |