Last. Xiaoqi Yang(HKPU: Hong Kong Polytechnic University)H-Index: 54
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An interior quasi-subgradient method is proposed based on the proximal distance to solve constrained nondifferentiable quasi-convex optimization problems in Hilbert spaces. It is shown that a newly introduced generalized Gâteaux subdifferential is a subset of a quasi-subdifferential. The convergence properties, including the global convergence and iteration complexity, are investigated under the assumption of the Holder condition of order p, when using the constant/diminishing/dynamic stepsize r...
Last. N. Pozas(UMA: University of Malaga)H-Index: 1
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This paper addresses the problem of approximating the set of all solutions for Multi-objective Markov Decision Processes. We show that in the vast majority of interesting cases, the number of solutions is exponential or even infinite. In order to overcome this difficulty we propose to approximate the set of all solutions by means of a limited precision approach based on White’s multi-objective value-iteration dynamic programming algorithm. We prove that the number of calculated solutions is trac...
Channel coding aims to minimize the errors that occur during the transmission of digital information from one place to another. Low-density parity-check codes can detect and correct transmission errors if one encodes the original information by adding redundant bits. In practice, heuristic iterative decoding algorithms are used to decode the received vector. However, these algorithms may fail to decode if the received vector contains multiple errors. We consider decoding the received vector with...
Last. Marcus Volz(University of Melbourne)H-Index: 2
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A Euclidean skeleton is a set of edges in the interior (or on the boundary) of a polygon that intersects any line segment that joins two points outside of the polygon and that intersects the polygon. In this paper we study minimum cardinality Euclidean skeletons and develop an algorithm for constructing them. We first prove a number of structural properties of minimum skeletons and use these to develop a canonical form. We then design an exact algorithm which initially generates a set of canonic...
Every continuously differentiable function can be represented as a difference between a convex function and an additively separable convex function. We show that a DC function with this structure can be optimized using the rectangular algorithm for separable nonconvex optimization, and develop a revision to this algorithm for practical use. We also report some numerical results which indicate the effectiveness of the revision.
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