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That can be done by combining with other approaches (heuristic or local search methods, solutions of convex relaxation problems in global optimization methods).
We developed an algorithm where an infinite number of constraints can be implemented as several relaxation problems that are solved iteratively.
The algorithm searches for an optimal solution to the binary integer programming problem by solving a series of LP relaxation problems, in which the binary integer requirement on the variables is replaced by the weaker constraint 0≤b k,l ≤1.
The proposed algorithm which connects branch-and-bound method with the pruning technique successfully is convergent to the global minimum, according to the successive refinement of the linear relaxation of feasible region of the objective function and the solutions of a series of linear relaxation problems.
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This paper analytically studies the Lagrangian relaxation of WDP and expounds a novel technique for efficiently solving the relaxation problem.
DCA is used to calculate upper bound while lower bound is obtained from a linear relaxation problem.
By relaxing the binary assignment variables, a joint optimization algorithm was proposed to solve the relaxation problem.
The other methods considered are linearized and comparisons are made for a relaxation problem and a radiation diffusion problem.
It consists in the resolution of a mechanical relaxation problem between the various sub-volumes present in a computational cell.
A new measurement matrix is designed and L1 regularization method is used to convert ECT inverse problem to a convex relaxation problem which contains prior knowledge.
This approach provides a formulation of a convex relaxation problem that can be efficiently modeled and solved using compressed sensing (CS) algorithms.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com