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AMPL: A Modeling Language for Mathematical Programming
Robert Fourer,Brian W. Kernighan +1 more
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TLDR
An efficient translator is implemented that takes as input a linear AMPL model and associated data, and produces output suitable for standard linear programming optimizers.Abstract:
Practical large-scale mathematical programming involves more than just the application of an algorithm to minimize or maximize an objective function. Before any optimizing routine can be invoked, considerable effort must be expended to formulate the underlying model and to generate the requisite computational data structures. AMPL is a new language designed to make these steps easier and less error-prone. AMPL closely resembles the symbolic algebraic notation that many modelers use to describe mathematical programs, yet it is regular and formal enough to be processed by a computer system; it is particularly notable for the generality of its syntax and for the variety of its indexing operations. We have implemented an efficient translator that takes as input a linear AMPL model and associated data, and produces output suitable for standard linear programming optimizers. Both the language and the translator admit straightforward extensions to more general mathematical programs that incorporate nonlinear expressions or discrete variables.read more
Citations
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Journal ArticleDOI
Part II. Future perspective on optimization
TL;DR: This work discusses recent developments in deterministic global optimization algorithms applied to both nonlinear programs and mixed-integer programs, and discusses logic-based optimization and its influence in both modeling and solving mixed- integer optimization problems.
Proceedings ArticleDOI
Mixed-integer programming for control
Arthur Richards,Jonathan P. How +1 more
TL;DR: In this article, mixed-integer programming (MIP) is used to find optimal trajectories subject to integer constraints, which can encode discrete decisions or nonconvexity, for example.
Proceedings Article
An empirical study of greedy local search for satisfiability testing
Bart Selman,Henry Kautz +1 more
TL;DR: This paper describes the space traversed by GSAT, and discusses two general, domain-independent extensions that dramatically improve GSAT's performance on structured problems: the use of clause weights, and a way to average in near-solutions when initializing lhe procedure before each try.
Journal ArticleDOI
A comparison of complete global optimization solvers
TL;DR: Results are reported of testing a number of existing state of the art solvers for global constrained optimization and constraint satisfaction on a set of over 1000 test problems in up to 1000 variables, collected from the literature.
Journal ArticleDOI
On the optimal design of water distribution networks: a practical MINLP approach
TL;DR: The solutions obtained are immediately usable in practice because they are characterized by an allocation of diameters to pipes that leads to a correct hydraulic operation of the network, unlike most of the other methods presented in the literature.
References
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Book
AMPL: a mathematical programming language
Robert Fourer,Brian W. Kernighan +1 more
TL;DR: A translator is implemented that takes as input a linear AMPL model and associated data, and produces output suitable for standard linear programming optimizers.
Book ChapterDOI
On the development of a general algebraic modeling system in a strategic planning environment
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