convex optimization theory (60 resultados)

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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This volume includes chapters on topics presented at the conference on Recent Trends in Convex Optimization: Theory, Algorithms and Applications (RTCOTAA-2020), held at the Department of Mathematics, Indian Institute of Technology Patna, Bihar, India, from 29-31 October 2020. Itdiscusses a comprehensive exploration of the realm of optimization, encompassing both the theoretical underpinnings and the multifaceted real-life implementations of the optimization theory. It meticulously features essential optimization concepts, such as convex analysis, generalized convexity, monotonicity, etc., elucidating their theoretical advancements and significance in the optimization sphere. Multiobjective optimization is a pivotal topic which addresses the inherent difficulties faced in conflicting objectives. The book delves into various theoretical concepts and covers some practical algorithmic approaches to solve multiobjective optimization, such as the line search and the enhanced non-monotone quasi-Newton algorithms. It also deliberates on several other significant topics in optimization, such as the perturbation approach for vector optimization, and solution methods for set-valued optimization. Nonsmooth optimization is extensively covered, with in-depth discussions on various well-known tools of nonsmooth analysis, such as convexificators, limiting subdifferentials, tangential subdifferentials, quasi-differentials, etc.Notable optimization algorithms, such as the interior point algorithm and Lemke's algorithm, are dissected in detail, offering insights into their applicability and effectiveness. The book explores modern applications of optimization theory, for instance, optimized image encryption, resource allocation, target tracking problems, deep learning, entropy optimization, etc. Ranging from gradient-based optimization algorithms to metaheuristic approaches such as particle swarm optimization, the book navigates through the intersection of optimization theory and deep learning, thereby unravelling new research perspectives in artificial intelligence, machine learning and other fields of modern science. Designed primarily for graduate students and researchers across a variety of disciplines such as mathematics, operations research, electrical and electronics engineering, computer science, robotics, deep learning, image processing and artificial intelligence, this book serves as a comprehensive resource for someone interested in exploring the multifaceted domain of mathematical optimization and its myriad applications.

paperback. Condición: New. Paperback. Pub Date: 2011 01 of Pages: 403 Publisher: Tsinghua University Press convex optimization theory of Demetrio extraction. Professor Bosaikesi optimization theory internationally renowned scholars. the U.S. National Academy of Engineering. the incumbent Massachusetts Institute of Electrical Engineering and Computer Science Department professor. has taught in the Department of Economics and the University of Illinois Department of Electrical Engineering of Stanford University engineeri.

paperback. Condición: New. Language:Chinese.Paperback. Pub Date: 2015-10-01 Publisher: Tsinghua University Press three years ago. in October 2000. in order to systematically relevant reference for well-known foreign university textbooks. to promote international and reform of university curricula of university teaching process. planning Tsinghua University Press published the internationally renowned University of original teaching materials - disciplines of information technology and electrical engineering disciplines.

paperback. Condición: New. Paperback. Pub Date: 2019-12-01 Pages: 249 Language: Chinese Publisher: Science Press convex optimization theory and method system introduces the theory and methods of convex optimization. including convex sets. convex functions. convex optimization problem. dual problem. unconstrained projections more Steepest Descent method and Newton's method. with linear equality constraints convex optimization problem .

paperback. Condición: New. Paperback. Pub Date: 2020-07-01 Pages: 228 Language: Chinese Publisher: Science Press Convex Optimization Theory and Algorithms systematically expounds the theory and algorithm of convex optimization. First introduce the necessary basic knowledge of convex analysis. and then discuss Duality theory and optimality conditions. as the basis. they play a very important role in the theoretical analysis of convex optimization algorithms. *The convex optimization algorithm will be described later. Co.

paperback. Condición: New. Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Paperback. Pub Date: 2013 01 of Pages: 510 in Publisher: Science Press convex optimization theory is the field of signal processing theory has important application value analysis tools. the last two decades. a large number of signal processing based on convex optimization theory made breakthroughs. The outstanding books abroad of Information Science and Technology Series Electronics and Communications Technology: signal processing and communication of convex optimization theory (the English version). classic and cutting-edge issues in the communication and signal processing for context for visitors to learn all kinds of convex optimization analysis modeling methods and basic theory. Content. including the diagram model theory. based on the gradient of the signal reconstruction algorithm. semidefinite relaxation (SDP) algorithm based on the SDP's radar signal design. image processing. blind letter source separation. modern sampling theory. especially in broadband mobile communication MIMO signal detection . the beamforming theory in cognitive radio. distributed multi-objective optimization theory and game theory. The book can serve as researchers in the field of electronics and communication engineering. engineering and technical personnel. reference books. are also available for professional high grade undergraduate. graduate read. Contents: List of contributorPreface1 Automatic code generation for real.time convex optimizationJacob Mattingley and Stephen Boyd1.1 Introduction1.2 Solver and specification languages1.3 Examples1.4 Algorithm corideratior1.5 Code generation1.6 CVXMOD: a preliminary implementation1.7 Numerical examples1. 8 Summary. conclusior. and implicatiorAcknowledgmentsReferences2 Gradient-based algorithms with applicatior to signal-recovery problemsArnir Beck and Marc Teboulle2.1 Introduction2.2 The general optimization model2.3 Building gradient-based schemes2.4 Convergence results tor the proximal-gradient method2.5 A fast proximal-gradient method2.6 Algorithms for It-based regularization problems2.7 TV-based restoration problems2.8 The source-localization problem2.9 Bibliographic notesReferences3 Graphical models of autoregressive processesJitkomut Songsiri. Joachim Dahl. and Lieven Vandenberghe3.1 Introduction3. 2 Autoregressive processes3.3 Autoregressive graphical models3.4 Numerical examples3.5 ConclusionAcknowledgmentsReferences4 SDP relaxation of homogeneous quadratic optimization: approximation bounds and applicatior Zhi-Quan Luo and Tsung-Hui Chang4.1 Introduction4.2 Nonconvex QCQPs and SDP relaxation4.3 SDP relaxation for separable homogeneous QCQPs4.4 SDP relaxation for maximization homogeneous QCQPs4.5 SDP relaxation for fractional QCQPs4.6 More applicatior of SDP relaxation4.7 Summary and discussionAcknowledgmentsReferences5 ProbabilisUc analysis of semidefinite relaxation detector for multiple-input. multiple-output systemsAnthony Man-Cho So and Yinyu Ye5.1 Introduction5.2 Problem formulation5.3 Analysis of the SDR detector for the MPSK cortellatior5.4 Exterion to the QAM cortellatior5.5 Concluding remarksAcknowledgmentsReferences6 Semidefinite programming. matrix decomposition. and radar code designYongwei Huang. Antonio De Maio. and Shuzhong Zhang6 .1 Introduction and notation6.2 Matrix rank-1 decomposition6.3 Semidefinite programming6.4 Quadratically cortrained quadratic programming and its SDP relaxation6.5 Polynomially solvable QCQP problems6.6 The radar code-design problem6.7 Performance measures for code design6.8 Optimal code design6.9 Performance analysis6.10 ConclusiorReferences7 Convex analysis for non-negative blind source separation with application in imagingWing-Kin Ma. Tsung-Han Chan. Chong-Yung Chi. and Yue Wang7.1 Introduction7.2 Problem statement7.3 Review of some concepts in convex analysis7.4 Non-negative. blind source-separation criterion via CAMNS7.5 Systematic linear-p.

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