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Minimax Programs
 University of California Press
, 1997
"... We introduce an optimization problem called a minimax program that is similar to a linear program, except that the addition operator is replaced in the constraint equations by the maximum operator. We clarify the relation of this problem to some betterknown problems. We identify an interesting spec ..."
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Cited by 472 (5 self)
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highly effective algorithms for solution of various classes of linear programs. Linear programming represents one of the major achievements of the operations research and mathematical programming community. Supported in part by a National Science Foundation Graduate Fellowship. In this paper we
Program . . . Relationship to Mathematical Programming
 INTERFACES
, 2000
"... Arising from research in the computer science community, constraint programming is a relatively new technique for solving optimization problems. For those familiar with mathematical programming, there are a number of language barriers that exist that make it difficult to understand the concepts of ..."
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Cited by 1 (0 self)
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Arising from research in the computer science community, constraint programming is a relatively new technique for solving optimization problems. For those familiar with mathematical programming, there are a number of language barriers that exist that make it difficult to understand the concepts
and Mathematics Programs
"... Abstract: This article addresses Latino population growth in the United States and their ..."
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Abstract: This article addresses Latino population growth in the United States and their
Mathematical Programming for . . .
, 2009
"... The primary focus of this work is optimization algorithms for statistical learning tools and, in particular, the development and implementation of largescale algorithms for sparse principal component analysis (PCA) and kernel optimization. Sparse PCA seeks sparse factors, or linear combinations of ..."
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The primary focus of this work is optimization algorithms for statistical learning tools and, in particular, the development and implementation of largescale algorithms for sparse principal component analysis (PCA) and kernel optimization. Sparse PCA seeks sparse factors, or linear combinations of the data variables, explaining a maximum amount of variance in the data while having only a limited number of nonzero coefficients. We first enhance a recent first order algorithm for a semidefinite relaxation to sparse PCA using numerically cheaper approximate gradients, allowing us to work with larger data sets. These results are applied to some classic clustering and feature selection problems arising in biology. We next examine classification problems, specifically using support vector machines (SVM), which are heavily dependent on the choice of an input kernel matrix. Kernel learning seeks to improve classification performance by minimizing an upper bound on test error over a set of kernel matrices. Current classification methods, such as SVM, require positive semidefinite kernel matrices. We first address this limitation using kernel learning to incorporate indefinite kernels into SVM, and describe
Genetic Programming
, 1997
"... Introduction Genetic programming is a domainindependent problemsolving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring ..."
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Cited by 1046 (12 self)
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Introduction Genetic programming is a domainindependent problemsolving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring
An axiomatic basis for computer programming
 COMMUNICATIONS OF THE ACM
, 1969
"... In this paper an attempt is made to explore the logical foundations of computer programming by use of techniques which were first applied in the study of geometry and have later been extended to other branches of mathematics. This involves the elucidation of sets of axioms and rules of inference w ..."
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Cited by 1736 (4 self)
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In this paper an attempt is made to explore the logical foundations of computer programming by use of techniques which were first applied in the study of geometry and have later been extended to other branches of mathematics. This involves the elucidation of sets of axioms and rules of inference
An Extended Mathematical Programming Framework
, 2009
"... Extended mathematical programs are collections of functions and variables joined together using specific optimization and complementarity primitives. This paper outlines a mechanism to describe such an extended mathematical program by means of annotating the existing relationships within a model to ..."
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Cited by 5 (1 self)
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Extended mathematical programs are collections of functions and variables joined together using specific optimization and complementarity primitives. This paper outlines a mechanism to describe such an extended mathematical program by means of annotating the existing relationships within a model
Mathematical Programming in Data Mining
 Data Mining and Knowledge Discovery
, 1996
"... Mathematical programming approaches to three fundamental problems will be described: feature selection, clustering and robust representation. The feature selection problem considered is that of discriminating between two sets while recognizing irrelevant and redundant features and suppressing them. ..."
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Cited by 31 (3 self)
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Mathematical programming approaches to three fundamental problems will be described: feature selection, clustering and robust representation. The feature selection problem considered is that of discriminating between two sets while recognizing irrelevant and redundant features and suppressing them
Results 1  10
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636,768