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Smoothing Methods for Convex Inequalities and Linear Complementarity Problems
 Mathematical Programming
, 1993
"... A smooth approximation p(x; ff) to the plus function: maxfx; 0g, is obtained by integrating the sigmoid function 1=(1 + e \Gammaffx ), commonly used in neural networks. By means of this approximation, linear and convex inequalities are converted into smooth, convex unconstrained minimization probl ..."
Abstract

Cited by 62 (6 self)
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A smooth approximation p(x; ff) to the plus function: maxfx; 0g, is obtained by integrating the sigmoid function 1=(1 + e \Gammaffx ), commonly used in neural networks. By means of this approximation, linear and convex inequalities are converted into smooth, convex unconstrained minimization problems, the solution of which approximates the solution of the original problem to a high degree of accuracy for ff sufficiently large. In the special case when a Slater constraint qualification is satisfied, an exact solution can be obtained for finite ff. Speedup over MINOS 5.4 was as high as 515 times for linear inequalities of size 1000 \Theta 1000, and 580 times for convex inequalities with 400 variables. Linear complementarity problems are converted into a system of smooth nonlinear equations and are solved by a quadratically convergent Newton method. For monotone LCP's with as many as 400 variables, the proposed approach was as much as 85 times faster than Lemke's method. Key Words: Smo...
A Bundle Type DualAscent Approach to Linear Multicommodity MinCost Flow Problems
, 1999
"... ... MinCost Flow problem, where the mutual capacity constraints are dualized and the resulting Lagrangean Dual is solved with a dualascent algorithm belonging to the class of Bundle methods. Although decomposition approaches to blockstructured Linear Programs have been reported not to be competit ..."
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Cited by 25 (14 self)
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... MinCost Flow problem, where the mutual capacity constraints are dualized and the resulting Lagrangean Dual is solved with a dualascent algorithm belonging to the class of Bundle methods. Although decomposition approaches to blockstructured Linear Programs have been reported not to be competitive with generalpurpose software, our extensive computational comparison shows that, when carefully implemented, a decomposition algorithm can outperform several other approaches, especially on problems where the number of commodities is “large” with respect to the size of the graph. Our specialized Bundle algorithm is characterized by a new heuristic for the trust region parameter handling, and embeds a specialized Quadratic Program solver that allows the efficient implementation of strategies for reducing the number of active Lagrangean variables. We also exploit the structural properties of the singlecommodity MinCost Flow subproblems to reduce the overall computational cost. The proposed approach can be easily extended to handle variants of the problem.
ValueEstimation Function Method for Constrained Global Optimization
 Journal of Optimization Theory and Applications
, 1998
"... A novel valueestimation function method for global optimization problems with inequality constraints is proposed in this paper. The valueestimation function formulation is an auxiliary unconstrained optimization problem with a univariate parameter that represents an estimated optimal value of the ..."
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Cited by 1 (1 self)
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A novel valueestimation function method for global optimization problems with inequality constraints is proposed in this paper. The valueestimation function formulation is an auxiliary unconstrained optimization problem with a univariate parameter that represents an estimated optimal value of the objective function of the original optimization problem. A solution is optimal to the original problem if and only if it is also optimal to the auxiliary unconstrained optimization with the parameter set at the optimal objective value of the original problem, which turns out to be the unique root of a basic valueestimation function. A logarithmicexponential valueestimation function formulation is further developed to acquire computational tractability and efficiency. The optimal objective value of the original problem as well as the optimal solution are sought iteratively by applying either a generalized Newton 's method or a bisection method to the logarithmicexponential valueestimatio...
1.1 The Multicommodity Flow Problem
, 2005
"... We present a new efficient approach for solving the multicommodity flow problem as a sequence of subproblems, each on a very sparse but connected network. We show that each subproblem can be contracted to a problem on a much smaller graph. We then solve these problems using the simplex method. We te ..."
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We present a new efficient approach for solving the multicommodity flow problem as a sequence of subproblems, each on a very sparse but connected network. We show that each subproblem can be contracted to a problem on a much smaller graph. We then solve these problems using the simplex method. We test the algorithm on benchmark instances, and show its improvement over the usual simplex algorithm.