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Convex programming --- Linear programming --- Relaxation methods (Mathematics)
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Programming (Mathematics) --- Convex programming --- Interior-point methods --- #TELE:SISTA --- Convex programming. --- Interior-point methods. --- Programmation (mathématiques) --- Calcul des variations --- Programmation mathematique --- Programmation convexe
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Planning (firm) --- 519.7 --- Convex programming --- Programming (Mathematics) --- Mathematical cybernetics --- 519.7 Mathematical cybernetics --- Théorie des jeux --- Programmation mathematique --- Analyse convexe --- Programmation convexe
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Operational research. Game theory --- Convex programming --- Convex sets --- Convex functions --- 330.105 --- 519.8 --- Wiskundige economie. Wiskundige methoden in de economie --- Operational research --- Convex programming. --- Convex sets. --- Convex functions. --- 519.8 Operational research --- 330.105 Wiskundige economie. Wiskundige methoden in de economie --- Programmation mathematique --- Programmation convexe
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517.51 --- Functions of a real variable. Real functions --- Convex functions. --- Convex sets. --- Convex programming. --- 517.51 Functions of a real variable. Real functions --- Convex functions --- Convex programming --- Convex sets --- Sets, Convex --- Convex domains --- Set theory --- Programming (Mathematics) --- Functions, Convex --- Functions of real variables --- Convex geometry. --- Géométrie convexe. --- Fonctions convexes. --- Géometrie convexe --- Analyse convexe --- Programmation mathematique --- Programmation convexe
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In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. For problems like maximum flow, maximum matching, and submodular function minimization, the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. The goal of this self-contained book is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. This modern text explains the success of these algorithms in problems of discrete optimization, as well as how these methods have significantly pushed the state of the art of convex optimization itself.
Mathematical optimization. --- Convex programming. --- Convex functions. --- Functions, Convex --- Functions of real variables --- Programming (Mathematics) --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis
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An updated and revised edition of the 1986 title Convexity and Optimization in Banach Spaces, this book provides a self-contained presentation of basic results of the theory of convex sets and functions in infinite-dimensional spaces. The main emphasis is on applications to convex optimization and convex optimal control problems in Banach spaces. A distinctive feature is a strong emphasis on the connection between theory and application. This edition has been updated to include new results pertaining to advanced concepts of subdifferential for convex functions and new duality results in convex programming. The last chapter, concerned with convex control problems, has been rewritten and completed with new research concerning boundary control systems, the dynamic programming equations in optimal control theory and periodic optimal control problems. Finally, the structure of the book has been modified to highlight the most recent progression in the field including fundamental results on the theory of infinite-dimensional convex analysis and includes helpful bibliographical notes at the end of each chapter.
Banach spaces. --- Convex functions. --- Hilbert space. --- Mathematical optimization. --- Banach spaces --- Hilbert space --- Convex functions --- Convex programming --- Mathematics --- Civil & Environmental Engineering --- Physical Sciences & Mathematics --- Engineering & Applied Sciences --- Operations Research --- Calculus --- Convex programming. --- Functions, Convex --- Mathematics. --- Optimization. --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Math --- Science --- Programming (Mathematics) --- Functions of real variables --- Hyperspace --- Inner product spaces --- Functions of complex variables --- Generalized spaces --- Topology
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Operational research. Game theory --- Convex programming --- Mathematical optimization --- Maxima and minima --- Programmation convexe --- Optimisation mathématique --- Maxima et minima --- 681.3*G16 --- Optimization: constrained optimization; gradient methods; integer programming; least squares methods; linear programming; nonlinear programming (Numericalanalysis) --- Mathematical optimization. --- Maxima and minima. --- Convex programming. --- 681.3*G16 Optimization: constrained optimization; gradient methods; integer programming; least squares methods; linear programming; nonlinear programming (Numericalanalysis) --- Optimisation mathématique --- Minima --- Mathematics --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Operations research --- Simulation methods --- System analysis --- Programming (Mathematics) --- Programmation (mathématiques) --- Maximums et minimums. --- Programmation (mathématiques) --- Recherche opérationnelle --- Théorie des jeux --- Programmation mathematique
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Optimization is ubiquitous in power system engineering. Drawing on powerful, modern tools from convex optimization, this rigorous exposition introduces essential techniques for formulating linear, second-order cone, and semidefinite programming approximations to the canonical optimal power flow problem, which lies at the heart of many different power system optimizations. Convex models in each optimization class are then developed in parallel for a variety of practical applications like unit commitment, generation and transmission planning, and nodal pricing. Presenting classical approximations and modern convex relaxations side-by-side, and a selection of problems and worked examples, this is an invaluable resource for students and researchers from industry and academia in power systems, optimization, and control.
Electric power systems --- Electric power distribution --- Convex programming --- Mathematical optimization --- Mathematical models --- Mathematics --- 621.315 --- Convex programming. --- Mathematical optimization. --- Programming (Mathematics) --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Electricity --- Power distribution, Electric --- Power transmission --- Electric power transmission --- Electrification --- Transmission of electric energy. Power distribution and telecommunication lines. Conductors. Insulating materials. Accessories. Design, construction of lines --- Mathematics. --- Distribution --- 621.315 Transmission of electric energy. Power distribution and telecommunication lines. Conductors. Insulating materials. Accessories. Design, construction of lines --- Electric power systems - Mathematical models --- Electric power distribution - Mathematics
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Mathematical analysis --- Banach spaces --- Convex functions --- Convex programming --- 517.97 --- Hilbert space --- Hyperspace --- Inner product spaces --- Programming (Mathematics) --- Functions, Convex --- Functions of real variables --- Functions of complex variables --- Generalized spaces --- Topology --- Calculus of variations. Mathematical theory of control --- 517.97 Calculus of variations. Mathematical theory of control
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