<>>>/BBox[0 0 612 792]/Length 164>>stream and optimization, but to the best of our knowledge, none of them provide a similar type of results. On the Communication Complexity of Lipschitzian Optimization for the Coordinated Model of Computation Author links open overlay panel Mehran Mesbahi a 1 … 10 0 obj endstream endstream endstream 56 0 obj %���� endstream <>>>/BBox[0 0 612 792]/Length 164>>stream Laboratory for Information and Decision Systems. Weizmann Institute of Science, Rehovot, Israel . ; Massachusetts Institute of Technology. Use, Smithsonian Request PDF | The Communication Complexity of Optimization | We consider the communication complexity of a number of distributed optimization problems. / Tsitsiklis, John N.; Luo, Zhi Quan. 16 0 obj In both cases, using dynamic batch sizes can achieve the linear speedup of convergence with communication com-plexity less than that of existing communication efﬁcient parallel SGD methods with ﬁxed batch sizes (Stich,2018; Yu et al.,2018). <>stream �0��=WqFLrj,��������slS�&䤈w�Y>x���ꆀ�[h@� 蜸5�,�Nbu�y�UK-`�ШBC�`vrWʽ�X Oj���%9?/�@Mʿ����543����������������,�U���S��H%��� 2*���IW+~vo5� We start with the problem of solving a linear system. endstream <>>>/BBox[0 0 612 792]/Length 164>>stream 24 0 obj Authors: Santosh S. Vempala. x�S�*�*T0T0 B�kh�g������i������ ��� endstream 14 0 obj x�+� � | 32 0 obj ∙ 0 ∙ share We consider the communication complexity of a number of distributed optimization problems. Get the latest machine learning methods with code. The tutorial contains two parts. Part of Advances in Neural Information Processing Systems 28 (NIPS 2015) Bibtex » Metadata » Paper » Reviews » Supplemental » Authors. 17 0 obj We start with the problem of solving a linear system. �0��=WqFLrj,��������slS�&䤈w�Y>x���ꆀ�[h@� 蜸5�,�Nbu�y�UK-`�ШBC�`vrWʽ�X Oj���%9?/�@Mʿ����543����������������,�U���S��H%��� 2*���IW+~vo5� 37 0 obj Get the latest machine learning methods with code. 1 0 obj The problem is usually stated as … <>stream <>stream endstream The Communication Complexity of Optimization x�ν �0��=WqFLrj,��������slS�&䤈w�Y>x���ꆀ�[h@� 蜸5�,�Nbu�y�UK-`�ШBC�`vrWʽ�X Oj���%9?/�@Mʿ����543����������������,�U���S��H%��� 2*���IW+~vo5� Block matrix multiplication. endobj Weizmann Institute of Science, Rehovot, Israel. endobj endstream endobj endstream View Profile, Ruosong Wang. endstream <>stream For general and in the point-to-point model, we show an upper bound and an lower bound. Browse SIDMA; SIAM J. on Financial Mathematics. We believe that these issues yield new and interest-ing questions in multi-player communication complexity. LaTeX with hyperref 7 0 obj x�S�*�*T0T0 B�kh�g������i������ ��� Perhaps the most closely-related paper is [22], which studied the communication complexity of distributed opti-mization, and showed that Ω(dlog(1/ǫ)) bits of communication are necessary between the machines, for d-dimensional convex problems. H��WK������Q
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�M6�M4���¶�na$ˑ�~�O��, Santosh S. Vempala, Ruosong Wang and David P. Woodruff, The Communication Complexity of Optimization. endstream Bibliography: leaf 10. Communication Complexity of Dual Decomposition Methods for Distributed Resource Allocation Optimization Sindri Magnusson, Chinwendu Enyioha, Na Li, Carlo Fischione, and Vahid Tarokh´ Abstract— Dual decomposition methods are among the most prominent approaches for ﬁnding primal/dual saddle point so-lutions of resource allocation optimization problems. On the Communication Complexity of Lipschitzian Optimization for the Coordinated Model of Computation . x�+� � | Communication Complexity of Convex Optimization* JOHN N. TSITSIKLIS AND ZHI-QUAN Luo Laboratory for Information and Decision Systems and the Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 We consider a situation where each of two processors has access to a different convex functionA, i = 1,2, defined on a common bounded domain. endobj 9 0 obj endobj 46 0 obj Author(s) Tsitsiklis, John N.; Luo, Zhi-Quan. <>>>/BBox[0 0 612 792]/Length 164>>stream The algorithm isn't practical due to the communication cost inherent in moving data to and from the temporary matrix T, but a more practical variant achieves Θ(n 2) speedup, without using a temporary matrix. <>stream endstream No code available yet. Overview; Fingerprint; Abstract. endobj Complexity management is a business methodology that deals with the analysis and optimization of complexity in enterprises. Furthermore, the proposed approach is also able to achieve O(m 3/2) sample complexity and O( 1) communication complexity for the online problem (3), re- Laboratory for Information and Decision Systems. x�+� � | x�+� � | endobj <>>>/BBox[0 0 612 792]/Length 164>>stream 19 0 obj �0��=WqFLrj,��������slS�&䤈w�Y>x���ꆀ�[h@� 蜸5�,�Nbu�y�UK-`�ШBC�`vrWʽ�X Oj���%9?/�@Mʿ����543����������������,�U���S��H%��� 2*���IW+~vo5� The connection to communication complexity is the following. endstream <>>>/BBox[0 0 612 792]/Length 164>>stream 30 0 obj In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.Artificial ants stand for multi-agent methods inspired by the behavior of real ants. 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