Results 11  20
of
35
StAMPL: A filtrationoriented modeling tool for stochastic programming, tech
, 2006
"... This research investigates how to create a modeling tool specifically for stochastic programming problems with recourse. By taking advantage of the special structure these problems have, we produce a modeling language whose syntax is less redundant, more modular, and more expressive than the notatio ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
(Show Context)
This research investigates how to create a modeling tool specifically for stochastic programming problems with recourse. By taking advantage of the special structure these problems have, we produce a modeling language whose syntax is less redundant, more modular, and more expressive than the notation commonly associated with stochastic programming. We then implement a system that can convert models written using this syntax to instances that can be solved using standard mechanisms. With this approach, we are able to represent models in a very clean, simple, and scalable format. 1.
SymbolicAlgebraic Computations in a Modeling Language for Mathematical Programming
 In Symbolic Algebraic Methods and Verification
, 2001
"... AMPL is a language and environment for expressing and manipulating mathematical programming problems, i.e., minimizing or maximizing an algebraic objective function subject to algebraic constraints. The AMPL processor simplifies problems, as discussed in more detail below, but calls on separate solv ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
AMPL is a language and environment for expressing and manipulating mathematical programming problems, i.e., minimizing or maximizing an algebraic objective function subject to algebraic constraints. The AMPL processor simplifies problems, as discussed in more detail below, but calls on separate solvers to actually
Computing in Operations Research using Julia
"... The state of numerical computing is currently characterized by a divide between highly efficient yet typically cumbersome lowlevel languages such as C, C++, and Fortran and highly expressive yet typically slow highlevel languages such as Python and MATLAB. This paper explores how Julia, a modern ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
The state of numerical computing is currently characterized by a divide between highly efficient yet typically cumbersome lowlevel languages such as C, C++, and Fortran and highly expressive yet typically slow highlevel languages such as Python and MATLAB. This paper explores how Julia, a modern programming language for numerical computing which claims to bridge this divide by incorporating recent advances in language and compiler design (such as justintime compilation), can be used for implementing software and algorithms fundamental to the field of operations research, with a focus on mathematical optimization. In particular, we demonstrate algebraic modeling for linear and nonlinear optimization and a partial implementation of a practical simplex code. Extensive crosslanguage benchmarks suggest that Julia is capable of obtaining stateoftheart performance. Key words: algebraic modeling; scientific computing; programming languages; metaprogramming; domainspecific languages 1.
The NLPLIB Graphical User Interface
, 1998
"... This paper presents a Graphical User Interface (GUI) for nonlinear programming in MATLAB. The GUI gives easy access to all features in the NLPLIB (NonLinear Programming LIBrary) Toolbox; a set of MATLAB solvers, test problems, graphical and computational utilities for unconstrained and constrained o ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
This paper presents a Graphical User Interface (GUI) for nonlinear programming in MATLAB. The GUI gives easy access to all features in the NLPLIB (NonLinear Programming LIBrary) Toolbox; a set of MATLAB solvers, test problems, graphical and computational utilities for unconstrained and constrained optimization, quadratic programming, unconstrained and constrained nonlinear least squares and exponential sum øtting. NLPLIB TB, like the operations research toolbox OPERA TB, is a part of TOMLAB; an environment in MATLAB for research and teaching in optimization. TOMLAB currently solves small and medium size dense problems. Presently, NLPLIB TB implements more than 20 internal solvers. From NLPLIB TB it is possible to call solvers in the MATLAB Optimization Toolbox and generalpurpose solvers implemented in Fortran or C. Fortran and C solvers are called from MATLAB using a MEXøle interface. A problem is solved either by direct call to a parameter driven driver routine, or interactively, usi...
Linking a Symbolic Solver to AMPL
"... symopt, a package of the computer algebra system reduce, is a symbolic solver for parametric mathematical programming. In order to make symopt applicable for interested people not familiar with reduce we implemented an interface to ampl  an algebraic modeling language for mathematical programm ..."
Abstract
 Add to MetaCart
symopt, a package of the computer algebra system reduce, is a symbolic solver for parametric mathematical programming. In order to make symopt applicable for interested people not familiar with reduce we implemented an interface to ampl  an algebraic modeling language for mathematical programming. In this paper the linking process of a symbolic solver to a algebraic modeling system is described.
An `1 Elastic InteriorPoint Methods for Mathematical Programs with Complementarity Constraints
, 2009
"... Abstract. We propose an interiorpoint algorithm based on an elastic formulation of the `1penalty merit function for mathematical programs with complementarity constraints. The method generalizes that of Gould, Orban, and Toint (2003) and naturally converges to a strongly stationary point or delive ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract. We propose an interiorpoint algorithm based on an elastic formulation of the `1penalty merit function for mathematical programs with complementarity constraints. The method generalizes that of Gould, Orban, and Toint (2003) and naturally converges to a strongly stationary point or delivers a certificate of degeneracy without recourse to secondorder intermediate solutions. Remarkably, the method allows for a unified treatment of both general, unstructured, and structured degenerate problems, such as problems with complementarity constraints, with no changes to accommodate one class or the other. Numerical results on a
Convexity and Concavity Detection in Computational Graphs
, 2008
"... In this paper, we examine sets of symbolic tools associated to modeling systems for mathematical programming which can be used to automatically detect the presence or lack of convexity and concavity in the objective and constraint functions. As a consequence, convexity of the feasible set may be ass ..."
Abstract
 Add to MetaCart
In this paper, we examine sets of symbolic tools associated to modeling systems for mathematical programming which can be used to automatically detect the presence or lack of convexity and concavity in the objective and constraint functions. As a consequence, convexity of the feasible set may be assessed to some extent. The coconut solver system [Sch04b] focuses on nonlinear global continuous optimization and possesses its own modeling language and data structures. The Dr.ampl [FO07] metasolver aims to analyze nonlinear diffentiable optimization models and hooks into the ampl Solver Library [Gay02]. The symbolic analysis may ◭ be supplemented with a numerical disproving phase when the former returns inconclusive results. We report numerical results using these tools on sets of test problems for both global and local optimization.