Lagrangean decomposition has been recognized as a promising approach for solving large-scale optimization problems. However, Lagrangean decomposition is critically dependent on the method of updating the Lagrangean multipliers used to decompose the original model. This paper presents a Lagrangean

decomposition subproblems, and the Lagrangean multipliers are updated through a subgradient method. Numerical examples are used to illustrate the proposed approach and the solutions are compared with the full space solutions with no decomposition. In general, slightly better solutions are found but in the terms

GMAP instance on peer-to-peer message exchange mechanisms. In the protocol we introduce a parameter that controls the range of “noise ” mixed with increment/decrement in a lagrangean multiplier. This parameter can be used to produce quick agreement between the agents on a feasible solution

the bottleneck link utilization is to be minimized. The LRH approach performs constraint relaxation and derives a lower-bound solution index according to a set of Lagrangean multipliers generated through subgradient-based it-erations. In parallel, using the generated Lagrangean multipliers, the LRH approach

for the non-anticipativity conditions. In addition to that, we present a new hybrid algorithm for updating the Lagrangean multiplier set, based on the combination of cutting-plane, subgradient and trust-region strategies. Numerical results suggests that different formulations of the non

The Augmented Lagrangean Relaxation (ALR) method is one of the most powerful techniques to solve the Short-Term Hydrothermal Coordination (STHC) problem. A crucial step when using the ALR method is the updating of the multipliers. In this paper we present a new multiplier updating procedure

vector of multipliers for the capacity constraints, the second for the assignment constraints and the other for the Lagrangean decomposition constraints. Relaxation multipliers are used with ecient constructive heuristics to ®nd good feasible solutions. The application of a Lagrangean/surrogate approach

Conventional Lagrangean preprocessing for the network Weight Constrained Shortest Path Christofides [3], calculates lower bounds on the cost of using each node and edge in a feasible path using a single optimal Lagrange multiplier for the relaxation of the WCSPP. These lower bounds are used

an associated time window. The Shortest Path decomposition of the VRPTW by Lagrangian relaxation re-quire the nding of the optimal Lagrangian multipliers. This problem is a convex non-dierentiable optimization problem. We propose a cutting-plane algorithm with a trust-region stabilizing device for nding

cost selection. The Lagrangean/surrogate multiplier modifies the reduced cost criterion, providing the selection of new productive columns. The p-median problem is the problem of locating p facilities (medians) on a network so as to minimize the sum of all the distances from each demand point to its