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This result has been extended to a wider class of optimization problems by Locatelli [19].
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Primal and dual interior-point methods (IPMs) have been well known as the most effective methods for solving wide classes of optimization problems, for example, the linear optimization (LO) problem, the quadratic optimization problem (QOP), the semidefinite optimization (SDO) problem, the second-order cone optimization (SOCO) problem, and the convex optimization problem (CP).
Two of the most promising heuristics from nature for a wide class of combinatorial optimization problems, genetic algorithms (GA) and ant colony optimization (ACO), share data structures and co-evolve in parallel in order to improve the performance of the constituent algorithms.
Furthermore, this unifies the analysis for a wide class of conic optimization problems.
Furthermore, this unifies the analysis for a wide class of conic optimization problems, which includes LO, CQO, SOCO, SDO, CQSDO, SCO and so on.
Comparisons indicate that DisABC performs very well and can be regarded as a promising method for solving wide class of binary optimization problems.
Therefore, the class of multiplicative -Banach-contraction mappings is a real wider class of Banach-contraction mappings.
We further show a lower bound of 32 for a wider class of D-benevolent instances.
This paper presents a generalization of these algorithms to a wider class of problems.
We obtain a generalization of known inequalities for a wider class of twice differentiable functions.
We also generalize the results to a wider class of computer-generated networks.
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