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First-Order Methods in Large-Scale Semidefinite Optimization

First-Order Methods in Large-Scale Semidefinite Optimization is een boek van Cuvillier Verlag;

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Handbook on Semidefinite, Conic and Polynomial Optimization

Semidefinite and conic optimization is a major and thriving research area within the optimization community. Although semidefinite;

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Polyhedral and Semidefinite Programming Methods in Combinatorial Optimization

Since the early 1960s, polyhedral methods have played a central role in both the theory and practice of combinatorial optimization. Since;

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Redundancy in Semidefinite Programming

inequality constraints. It discusses both probabilistic and deterministic methods. The first probabilistic method, called the semidefinite-stand-hit;

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Large-Scale Inverse Problems and Quantification of Uncertainty

optimization-based approaches to solving inverse problems. The aim is to cross-fertilize the perspectives of researchers in the areas of data;

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Large-Scale Scientific Computing

invited talks. The papers were organized in topical sections named as follows: control and optimization of dynamical systems; meshfree and;

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Non-centralized Optimization-Based Control Schemes for Large-Scale Energy Systems

system partitioning techniques. At first, the book describes two distributed optimization methods, which are iterative and require the local;

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Self-Regularity

, nonlinear complementarity problems, semidefinite optimization, and second-order conic optimization problems. The framework also covers large classes;

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Nonlinear Conjugate Gradient Methods for Unconstrained Optimization

Two approaches are known for solving large-scale unconstrained optimization problems-the limited-memory quasi-Newton method (truncated;

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Nonlinear Conjugate Gradient Methods for Unconstrained Optimization

Two approaches are known for solving large-scale unconstrained optimization problems-the limited-memory quasi-Newton method (truncated;

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Regularization, Optimization, Kernels, and Support Vector Machines

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine;

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Regularization, Optimization, Kernels, and Support Vector Machines

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine;

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Handbook of Semidefinite Programming

Semidefinite programming (SDP) is one of the most exciting and active research areas in optimization. It has and continues to attract;

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Handbook of Semidefinite Programming

Semidefinite programming (SDP) is one of the most exciting and active research areas in optimization. It has and continues to attract;

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Lectures on Modern Convex Optimization

Here is a book devoted to well-structured and thus efficiently solvable convex optimization problems, with emphasis on conic quadratic and;

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Introduction to Continuous Optimization

issues, first and second order methods with accelerated convergence, nonlinear rescaling (NR) theory and exterior point methods, just to mention;

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Parallel Random Search Algorithm

Random search methods are implemented to solve the wide variety of the large-scale discrete optimization problems when the implementation;

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Distributed Machine Learning and Gradient Optimization

This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the;

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Optimization in Renewable Energy Systems

unpredictable, leading to the need for optimization-based planning and operation in order to maximize economies while sustaining performance.

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Modern Optimization Modelling Techniques

The theory of optimization, understood in a broad sense, is the basis of modern applied mathematics, covering a large spectrum of topics;

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Nonlinear Optimization

Optimization is one of the most important areas of modern applied mathematics, with applications in fields from engineering and economics;

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System Modelling and Optimization

System Modelling and Optimization covers research issues within systems theory, optimization, modelling, and computing. It includes;

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Optimization of Power System Problems

of optimization algorithms into power systems has been discussed in several textbooks, but this is the first to include the integration methods and the;

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Mathematical Modeling, Simulation and Optimization for Power Engineering and Management

simulation of possibly large-scale interconnected energy systems and modern techniques for optimization purposes to guarantee stable and reliable;

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Large-Scale Scientific Computing

and optimization; Parallel algorithms and performance analysis; Large-scale computations of environmental, biomedical and engineering;

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Multidisciplinary Design Optimization in Computational Mechanics

This book provides a comprehensive introduction to the mathematical and algorithmic methods for the Multidisciplinary Design Optimization;

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Convex Optimization of Power Systems

Optimization is ubiquitous in power system engineering. Drawing on powerful, modern tools from convex optimization, this rigorous;

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