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Sparse Optimization Theory and Methods

science. Sparse Optimization Theory and Methods presents the state of the art in theory and algorithms for signal recovery under the sparsity;

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Sparse Optimization Theory and Methods

science. Sparse Optimization Theory and Methods presents the state of the art in theory and algorithms for signal recovery under the;

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Deep Learning through Sparse and Low-Rank Modeling

analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide;

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Statistical Inference via Convex Optimization

optimization theory can be used to devise and analyze near-optimal statistical inferences. Statistical Inference via Convex Optimization is an;

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Sparse Modeling

: Theory, Algorithms, and Applications provides an introduction to the growing field of sparse modeling, including application examples, problem;

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Sparse Modeling

Modeling: Theory, Algorithms, and Applications provides an introduction to the growing field of sparse modeling, including application examples;

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Image Understanding Using Sparse Representations

theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms;

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Intelligent Computing Theories and Application

Intelligence and Multimedia Technology; Genetic Algorithms; Biomedical Informatics Theory and Methods; Particle Swarm Optimization and Niche Technology;

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Financial Signal Processing and Machine Learning

portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures;

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Harry Markowitz

Neumann Prize from the Institute of Management Science and the Operations Research Institute of America in 1989 for his work in portfolio theory;

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Nonlinear Interval Optimization for Uncertain Problems

This book systematically discusses nonlinear interval optimization design theory and methods. Firstly, adopting a mathematical programming;

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Nonlinear Optimization

systems, integrates the theory and the methods of nonlinear optimization in a unified, clear, and mathematically rigorous fashion, with detailed;

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Nonsmooth Optimization Methods

Nonsmooth Optimization Methods and Applications provides an overview of this branch of mathematics, concentrating on the interaction;

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Optimization with Sparsity-Inducing Penalties

. Optimization with Sparsity-Inducing Penalties presents optimization tools and techniques dedicated to such sparsity-inducing penalties from a general;

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Introduction to Continuous Optimization

-semester course in Continuous Optimization. The topical coverage includes interior point methods, self-concordance theory and related complexity;

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Flexible and Generalized Uncertainty Optimization

This book presents the theory and methods of flexible and generalized uncertainty optimization. Particularly, it describes the theory;

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Flexible and Generalized Uncertainty Optimization

This book presents the theory and methods of flexible and generalized uncertainty optimization. Particularly, it describes the theory;

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An Introduction to Optimization

of Introduction to Optimization fills the need for accessible treatment of optimization theory and methods with an emphasis on engineering design. Basic;

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Towards Optimal Point Cloud Processing for 3D Reconstruction

loop detection, sifting, and optimization methods. The authors offer a fast point cloud registration method that utilizes optimized;

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Sparse Approximation with Bases

approximation. Taking into account the theoretical and practical demand for this kind of theory, the book systematically elaborates a theoretical;

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A Mathematical View of Interior-point Methods in Convex Optimization

of the theory and emphasizing the underlying geometry, A Mathematical View of Interior-Point Methods in Convex Optimization makes the theory;

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Statistical Learning with Sparsity

lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the;

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Statistical Learning With Sparsity

lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the;

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Intelligent Computing Theories and Methodologies

optimization; pattern recognition and signal processing; image processing; biomedical informatics theory and methods; differential evolution, particle;

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Intelligent Computing Theories and Methodologies

optimization; pattern recognition and signal processing; image processing; biomedical informatics theory and methods; differential evolution, particle;

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Graphs and Combinatorial Optimization: from Theory to Applications

This book highlights new and original contributions on Graph Theory and Combinatorial Optimization both from the theoretical point of view;

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Intelligent Computing Technology

submissions. The 84 papers included in this volume are organized in topical sections on evolutionary learning and genetic algorithms, fuzzy theory and;

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