We present an extension to the subgradient algorithm to produce primal as well as dual solutions. It can he seen as a fast way to carry out an approximation of Dantzig-Wolfe decomposition. This gives a Fast method for...
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We present an extension to the subgradient algorithm to produce primal as well as dual solutions. It can he seen as a fast way to carry out an approximation of Dantzig-Wolfe decomposition. This gives a Fast method for producing approximations for large scale linear programs. It is based on a new theorem in linear programming duality. We present successful experience with linear programs coming from set partitioning, set covering, max-cut and plant location.
This paper presents a new Variable target value method (VTVM) that can be used in conjunction with pure or deflected subgradient strategies. The proposed procedure assumes no a priori knowledge regarding bounds on the...
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This paper presents a new Variable target value method (VTVM) that can be used in conjunction with pure or deflected subgradient strategies. The proposed procedure assumes no a priori knowledge regarding bounds on the optimal value. The target values are updated iteratively whenever necessary, depending on the information obtained in the process of the algorithm. Moreover, convergence of the sequence of incumbent solution values to a near-optimum is proved using popular, practically desirable step-length rules. In addition, the method also allows a wide flexibility in designing subgradient deflection strategies by imposing only mild conditions on the deflection parameter. Some preliminary computational results are reported on a set of standard test problems in order to demonstrate the viability of this approach. (C) 2000 Elsevier Science B.V. All rights reserved.
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