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Error bounds for mixed integer linear optimization problems
"... Abstract We introduce computable apriori and aposteriori error bounds for optimality and feasibility of a point generated as the rounding of an optimal point of the LP relaxation of a mixed integer linear optimization problem. Treating the mesh size of integer vectors as a parameter allows us to ..."
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Abstract We introduce computable apriori and aposteriori error bounds for optimality and feasibility of a point generated as the rounding of an optimal point of the LP relaxation of a mixed integer linear optimization problem. Treating the mesh size of integer vectors as a parameter allows us
Online Learning for Stochastic Linear Optimization Problems
 in Proc. of Information Theory and Applications Workshop (ITA
, 2012
"... Abstract—We consider the stochastic online linear optimization problems under unknown cost models. At each time, an action is chosen from a compact subset in R d and a random cost with an unknown distribution (depending on the action) is incurred. The expected value of the random cost is assumed to ..."
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Cited by 1 (1 self)
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Abstract—We consider the stochastic online linear optimization problems under unknown cost models. At each time, an action is chosen from a compact subset in R d and a random cost with an unknown distribution (depending on the action) is incurred. The expected value of the random cost is assumed
Sensitivity Analysis for Linear Optimization Problem with Fuzzy Data in the . . .
, 2004
"... Linear programming problems with fuzzy coefficients in the objective function are considered. Emphasis is on the dependence of the optimal solution from linear perturbations of the membership functions of the objective function coefficients as well as on the computation of a robust solution of t ..."
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Linear programming problems with fuzzy coefficients in the objective function are considered. Emphasis is on the dependence of the optimal solution from linear perturbations of the membership functions of the objective function coefficients as well as on the computation of a robust solution
DECOMPOSITION ALGORITHM APPLIED TO NONLINEAR OPTIMIZATION PROBLEMS„
"... Decomposition algorithm applied to nonlinear optimization ..."
Neural Networks Give a Warm Start to Linear Optimization Problems
"... Abstract Hopfield neural networks and interior point methods are used in an integrated way to solve linear optimization problems. The neural network unveils a warm starting point for the primaldual interior point method. This approach was applied to a set of real world linear programming problems. ..."
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Abstract Hopfield neural networks and interior point methods are used in an integrated way to solve linear optimization problems. The neural network unveils a warm starting point for the primaldual interior point method. This approach was applied to a set of real world linear programming problems
On the value function of a mixed integer linear optimization problem and an algorithm for construction
, 2014
"... This paper addresses the value function of a general mixed integer linear optimization problem (MILP). The value function describes the change in optimal objective value as the righthand side is varied and understanding its structure is central to solving a variety of important classes of optimiza ..."
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This paper addresses the value function of a general mixed integer linear optimization problem (MILP). The value function describes the change in optimal objective value as the righthand side is varied and understanding its structure is central to solving a variety of important classes
Global Optimization with Polynomials and the Problem of Moments
 SIAM JOURNAL ON OPTIMIZATION
, 2001
"... We consider the problem of finding the unconstrained global minimum of a realvalued polynomial p(x) : R R, as well as the global minimum of p(x), in a compact set K defined by polynomial inequalities. It is shown that this problem reduces to solving an (often finite) sequence of convex linear ma ..."
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Cited by 577 (48 self)
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We consider the problem of finding the unconstrained global minimum of a realvalued polynomial p(x) : R R, as well as the global minimum of p(x), in a compact set K defined by polynomial inequalities. It is shown that this problem reduces to solving an (often finite) sequence of convex linear
Training Linear SVMs in Linear Time
, 2006
"... Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for highdimensional sparse data commonly encountered in applications like text classification, wordsense disambiguation, and drug design. These applications involve a large number of examples n ..."
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Cited by 549 (6 self)
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as well as a large number of features N, while each example has only s << N nonzero features. This paper presents a CuttingPlane Algorithm for training linear SVMs that provably has training time O(sn) for classification problems and O(sn log(n)) for ordinal regression problems. The algorithm
Linear pattern matching algorithms
 IN PROCEEDINGS OF THE 14TH ANNUAL IEEE SYMPOSIUM ON SWITCHING AND AUTOMATA THEORY. IEEE
, 1972
"... In 1970, Knuth, Pratt, and Morris [1] showed how to do basic pattern matching in linear time. Related problems, such as those discussed in [4], have previously been solved by efficient but suboptimal algorithms. In this paper, we introduce an interesting data structure called a bitree. A linear ti ..."
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Cited by 547 (0 self)
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In 1970, Knuth, Pratt, and Morris [1] showed how to do basic pattern matching in linear time. Related problems, such as those discussed in [4], have previously been solved by efficient but suboptimal algorithms. In this paper, we introduce an interesting data structure called a bitree. A linear
Results 1  10
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2,854,257