This paper proposes the design and implementation of a dynamic programming based algorithm for (distributed) constraint optimization, which exploits modern massively parallel architectures, such as those found in modern Graphical Processing Units (GPUs). The paper studies the proposed algorithm in both centralized and distributed optimization contexts. The experimental analysis, performed on un...

We discuss in this paper local uniqueness, continuity and differentiability properties of solutions of parameterized variational inequalities (generalized equations). To this end we use two types of techniques. One approach consists in formulating variational inequalities in a form of optimization problems, based on regularized gap functions, and applying a general theory of perturbation analys...

We show that the class of strongly connected graphical models with tree-width at most k can be properly efficiently PAC-learnt with respect to the Kullback-Leibler Divergence. Previous approaches to this problem, such as those of Chow ([1]), and Hoffgen ([7]) have shown that this class is PAC-learnable (though not necessarily efficiently unless k = 1) by reducing to a NP-complete combinatorial ...

This article presents an improved ant colony optimization (IACO) algorithm to calculate the shortest path for pneumatic robot manipulator. MATLAB Script node in LabVIEW was used to determine the optimum trajectories and sequent nodes of moving for robot system. The LabVIEW graphical development software was used to construct the graphical user interference (GUI) of the robot manipulator, monito...

We show that the class of strongly connected graphical models with treewidth at most k can be properly efficiently PAC-learnt with respect to the Kullback-Leibler Divergence. Previous approaches to this problem, such as those of Chow ([1]), and Hoffgen ([7]) have shown that this class is PAClearnable by reducing it to a combinatorial optimization problem. However, for k > 1, this problem is NPc...

Recent research has made significant progress on the problem of bounding log partition functions for exponential family graphical models. Such bounds have associated dual parameters that are often used as heuristic estimates of the marginal probabilities required in inference and learning. However these variational estimates do not give rigorous bounds on marginal probabilities, nor do they giv...

The partition function plays a key role in probabilistic modeling including condi-tional random fields, graphical models, and maximum likelihood estimation. Tooptimize partition functions, this article introduces a quadratic variational upperbound. This inequality facilitates majorization methods: optimization of com-plicated functions through the iterative solution of simpler s...

The connection points between MV and LV distribution networks are MV substations. Optimal sitting, sizing and timing of MV substation placement is the major planning problem in MV-LV distribution system planning projects. In this paper the optimal MV substation placement problem is solved using Imperialist Competitive Algorithm (ICA) as a new developed heuristic optimization algorithm. This pro...

The paper focuses on finding the m best solutions to a combinatorial optimization problems using Best-First or Branch-and-Bound search. We are interested in graphical model optimization tasks (e.g., Weighted CSP), which can be formulated as finding the m-best solutionpaths in a weighted search graph. Specifically, we present m-A*, extending the well-known A* to the m-best problem, and prove tha...