A study of the behavior of LP models is a badly neglected area. By model behavior I mean the way the answers of an LP model vary as the input data is varied. In some cases, we find the model behaves much like the system being modeled. But in other cases it has quite different…
ACM SIGMAP Bulletin Template
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About the ACM SIGMAP Bulletin format
ACM SIGMAP Bulletin is a peer-reviewed journal published by ACM, covering Advanced Optimization Algorithms Research, Optimization and Mathematical Programming, Scheduling and Optimization Algorithms.
| Publisher | ACM |
|---|---|
| Reference style | Numbered (ACM) Numbered — [1], [2] in the text [1] Ada Smith, Ben Jones, and Cara Lee. 2023. A representative article title. ACM SIGMAP Bulletin 12, 3, Article 45 (2023).
Formats any DOI in ACM SIGMAP Bulletin style. No sign-up. |
| Publishes research in | Advanced Optimization Algorithms Research Optimization and Mathematical Programming Scheduling and Optimization Algorithms Metaheuristic Optimization Algorithms Research Advanced Graph Theory Research |
| ISSN | 0163-5786 |
| h-index | 11 |
| i10-index | 11 |
| Total citations | 799 |
| Top institutions publishing here | IBM (United States) |
| Journal website | dl.acm.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in ACM SIGMAP Bulletin per year
Citation impact of ACM SIGMAP Bulletin by publication year
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Most-cited papers in ACM SIGMAP Bulletin
A historical survey of the origins of nonlinear programming is presented with emphasis placed on necessary conditions for optimality. The mathematical sources for the work of Karush, John, Kuhn, and Tucker are traced and compared. Their results are illustrated by duality theorems for nonlinear programs that antedate the modern development of the subject.
A listing is given of a Fortran subroutine that calculates the least value of a function of several variables subject to general equality and inequality constraints. The user must provide an auxiliary subroutine that computes the objective and constraint functions and their gradients for any vector of variables. The underlying algorithm is a variable Metric…
In earlier issues of this bulletin, Haverly and Hart discussed solving some simple pooling problems using LP recursion [1]-[3]. We thought it would be interesting to attempt to solve these problems using some nonlinear programming (NLP) codes. This note briefly discusses the codes and algorithms used, presents the results, and discusses some properties of the…
Linear programming (LP) is an area of applied mathematics that is concerned with the minimization (or maximization) of a real-valued linear function on a convex polyhedron. Since the underlying theory is relatively straightforward, the history of the field is dated by the development of algorithms for solving LP problems. Thus the first important contribution was…