A Recursive Decomposition Method for Large Scale Continuous Optimization

Recursive Decomposition for Nonconvex Optimization

Continuous optimization is an important problem in many areas of AI, including vision, robotics, probabilistic inference, and machine learning. Unfortunately, most real-world optimization problems are nonconvex, causing standard convex techniques to find only local optima, even with extensions like random restarts and simulated annealing. We observe that, in many cases, the local modes of the o...

A Decomposition-Coordination Approach for Large-Scale Optimization

Thevenin Decomposition 1 and Large - Scale Optimization

Thevenin’s theorem, one of the most celebrated results of electric circuit theory, provides a two-parameter characterization of the behavior of an arbitrarily large circuit, as seen from two of its terminals. We interpret the theorem as a sensitivity result in an associated minimum energy/network flow problem, and we abstract its main idea to develop a decomposition method for convex quadratic ...

A note on the decomposition method for large-scale box constrained optimization

In this paper, we investigate on the decomposition method that was presented by Yu and et al. [1]. We show a note about it. © 2015 Elsevier Inc. All rights reserved.

DISCRETE AND CONTINUOUS SIZING OPTIMIZATION OF LARGE-SCALE TRUSS STRUCTURES USING DE-MEDT ALGORITHM

Design optimization of structures with discrete and continuous search spaces is a complex optimization problem with lots of local optima. Metaheuristic optimization algorithms, due to not requiring gradient information of the objective function, are efficient tools for solving these problems at a reasonable computational time. In this paper, the Doppler Effect-Mean Euclidian Distance Threshold ...