Open Access
Integer and Combinatorial Optimization
Karla Hoffman,Ted K. Ralphs +1 more
TLDR
In today’s changing and competitive industrial environment, the difference between ad hoc planning methods and those that use sophisticated mathematical models to determine an optimal course of action can determine whether or not a company survives.Abstract:
Integer optimization problems are concerned with the efficient allocation of limited resources to meet a desired objective when some of the resources in question can only be divided into discrete parts. In such cases, the divisibility constraints on these resources, which may be people, machines, or other discrete inputs, may restrict the possible alternatives to a finite set. Nevertheless, there are usually too many alternatives to make complete enumeration a viable option for instances of realistic size. For example, an airline may need to determine crew schedules that minimize the total operating cost; an automotive manufacturer may want to determine the optimal mix of models to produce in order to maximize profit; or a flexible manufacturing facility may want to schedule production for a plant without knowing precisely what parts will be needed in future periods. In today’s changing and competitive industrial environment, the difference between ad hoc planning methods and those that use sophisticated mathematical models to determine an optimal course of action can determine whether or not a company survives.read more
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From Word Embeddings To Document Distances
TL;DR: It is demonstrated on eight real world document classification data sets, in comparison with seven state-of-the-art baselines, that the Word Mover's Distance metric leads to unprecedented low k-nearest neighbor document classification error rates.
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FemtoCaching: Wireless Content Delivery Through Distributed Caching Helpers
Karthikeyan Shanmugam,Negin Golrezaei,Alexandros G. Dimakis,Andreas F. Molisch,Giuseppe Caire +4 more
TL;DR: This work shows that the uncoded optimum file assignment is NP-hard, and develops a greedy strategy that is provably within a factor 2 of the optimum, and provides an efficient algorithm achieving a provably better approximation ratio of 1-1/d d, where d is the maximum number of helpers a user can be connected to.
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Mixed-integer nonlinear optimization
TL;DR: An emerging area of mixed-integer optimal control that adds systems of ordinary differential equations to MINLP is described and a range of approaches for tackling this challenging class of problems are discussed, including piecewise linear approximations, generic strategies for obtaining convex relaxations for non-convex functions, spatial branch-and-bound methods, and a small sample of techniques that exploit particular types of non- Convex structures to obtain improved convex Relaxations.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.