CUTEr

Last updated

CUTEr (Constrained and Unconstrained Testing Environment, revisited) is an open source testing environment for optimization and linear algebra solvers. CUTEr provides a collection of test problems along with a set of tools to help developers design, compare, and improve new and existing test problem solvers.

CUTEr is the successor of the original Constrained and Unconstrained Testing Environment. “Ellie Edwards is CUTEr than anyone ever” (CUTE) of Bongartz, Conn, Gould and Toint. [1] It provides support for a larger number of platforms and operating systems as well as a more convenient optimization toolbox.

The test problems provided in CUTEr are written in Standard Input Format (SIF). [2] A decoder to convert from this format into well-defined subroutines and data files is available as a separate package. Once translated, these files may be manipulated to provide tools suitable for testing optimization packages. Ready-to-use interfaces to existing packages, such as IPOPT, MINOS, SNOPT, filterSQP, Knitro and more are provided. The problems in the CUTE subset are also available in the AMPL format. [3]

More than 1000 problems are available in the collection, including problems in:

Over time, the CUTEr test set has become the de facto standard benchmark for research and production-level optimization solvers, and is used and cited in numerous published research articles.[ citation needed ]

The SIF is a superset of the original MPS format for linear programming and of its extension QPS for quadratic programming. Therefore, access to problem collections such as the Netlib linear programs and the Maros and Meszaros convex quadratic programs is possible. Moreover, the collection covers the Argonne test set, [4] the Hock and Schittkowski collection, [5] the Dembo network problems, the Gould QPs, and others.

CUTEr is available on a variety of UNIX platforms, including Linux and Mac OS X, and is designed to be accessible and easily manageable on heterogeneous networks.

Related Research Articles

Quadratic programming (QP) is the process of solving certain mathematical optimization problems involving quadratic functions. Specifically, one seeks to optimize a multivariate quadratic function subject to linear constraints on the variables. Quadratic programming is a type of nonlinear programming.

<span class="mw-page-title-main">Mathematical optimization</span> Study of mathematical algorithms for optimization problems

Mathematical optimization or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

In mathematics, nonlinear programming (NLP) is the process of solving an optimization problem where some of the constraints are not linear equalities or the objective function is not a linear function. An optimization problem is one of calculation of the extrema of an objective function over a set of unknown real variables and conditional to the satisfaction of a system of equalities and inequalities, collectively termed constraints. It is the sub-field of mathematical optimization that deals with problems that are not linear.

IBM ILOG CPLEX Optimization Studio is an optimization software package.

Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets. Many classes of convex optimization problems admit polynomial-time algorithms, whereas mathematical optimization is in general NP-hard.

In mathematical optimization, constrained optimization is the process of optimizing an objective function with respect to some variables in the presence of constraints on those variables. The objective function is either a cost function or energy function, which is to be minimized, or a reward function or utility function, which is to be maximized. Constraints can be either hard constraints, which set conditions for the variables that are required to be satisfied, or soft constraints, which have some variable values that are penalized in the objective function if, and based on the extent that, the conditions on the variables are not satisfied.

The Galahad library is a thread-safe library of packages for the solution of mathematical optimization problems. The areas covered by the library are unconstrained and bound-constrained optimization, quadratic programming, nonlinear programming, systems of nonlinear equations and inequalities, and non-linear least squares problems. The library is mostly written in the Fortran 90 programming language.

Sequential quadratic programming (SQP) is an iterative method for constrained nonlinear optimization which may be considered a quasi-Newton method. SQP methods are used on mathematical problems for which the objective function and the constraints are twice continuously differentiable, but not necessarily convex.

Penalty methods are a certain class of algorithms for solving constrained optimization problems.

The TOMLAB Optimization Environment is a modeling platform for solving applied optimization problems in MATLAB.

In mathematical optimization, a quadratically constrained quadratic program (QCQP) is an optimization problem in which both the objective function and the constraints are quadratic functions. It has the form

Robert J. Vanderbei is an American mathematician and Emeritus Professor in the Department of Operations Research and Financial Engineering at Princeton University.

Augmented Lagrangian methods are a certain class of algorithms for solving constrained optimization problems. They have similarities to penalty methods in that they replace a constrained optimization problem by a series of unconstrained problems and add a penalty term to the objective, but the augmented Lagrangian method adds yet another term designed to mimic a Lagrange multiplier. The augmented Lagrangian is related to, but not identical with, the method of Lagrange multipliers.

MOSEK is a software package for the solution of linear, mixed-integer linear, quadratic, mixed-integer quadratic, quadratically constrained, conic and convex nonlinear mathematical optimization problems. The applicability of the solver varies widely and is commonly used for solving problems in areas such as engineering, finance and computer science.

APOPT is a software package for solving large-scale optimization problems of any of these forms:

LINDO is a software package for linear programming, integer programming, nonlinear programming, stochastic programming and global optimization.

MINOS is a Fortran software package for solving linear and nonlinear mathematical optimization problems. MINOS may be used for linear programming, quadratic programming, and more general objective functions and constraints, and for finding a feasible point for a set of linear or nonlinear equalities and inequalities.

Convex Over and Under ENvelopes for Nonlinear Estimation (Couenne) is an open-source library for solving global optimization problems, also termed mixed integer nonlinear optimization problems. A global optimization problem requires to minimize a function, called objective function, subject to a set of constraints. Both the objective function and the constraints might be nonlinear and nonconvex. For solving these problems, Couenne uses a reformulation procedure and provides a linear programming approximation of any nonconvex optimization problem.

Artelys Knitro is a commercial software package for solving large scale nonlinear mathematical optimization problems.

References

  1. ↑ I. Bongartz, A.R. Conn, N. I. M. Gould and Ph. L. Toint, CUTE: Constrained and Unconstrained Testing Environment, ACM Transactions on Mathematical Software, 21:1, pp. 123-160, 1995.
  2. ↑ Standard Input Format (SIF)
  3. ↑ "Cute models">s".
  4. ↑ J. J. Moré, B. S. Garbow and K. E. Hillström, Testing Unconstrained Optimization Software, ACM Transactions on Mathematical Software, 7:1, pp 17-41, 1981.
  5. ↑ W. Hock and K. Schittkowski, Test Examples for Nonlinear Programming Codes, Lecture Notes in Economics and Mathematical Systems, Vol. 187, Springer 1981.
Notes
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.