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Numerical Optimization : Theoretical and Practical Aspects
This book starts with illustrations of the ubiquitous character of optimization, and describes numerical algorithms in a tutorial way. It covers fundamental algorithms as well as more specialized and advanced topics for unconstrained and constrained problems. Most of the algorithms are explained in a detailed manner, allowing straightforward implementation. Theoretical aspects of the approaches chosen are also addressed with care, often using minimal assumptions. It's contains computational exercises in the form of case studies which help understanding optimization methods beyond their theoretical, description, when coming to actual implementation. Besides, the nonsmooth optimization part has been substantially reorganized and expanded.
Numerical Methods Using Java : For Data Science, Analysis, and Engineering
Covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. You will: Program in Java using a high-performance numerical library / Learn the mathematics for a wide range of numerical computing algorithms / Convert ideas and equations into code / Put together algorithms/ and classes to build your own engineering solution / Build solvers for industrial optimization problems / Do data analysis using basic and advanced statistics
Neuroscribe = نيوروسكرايب
Neuroscribe is a cutting-edge deep learning framework designed to address the complexities and inefficiencies encountered in existing frameworks like PyTorch and TensorFlow. Aimed at streamlining model development and enhancing performance across diverse hardware environments, NeuroScribe offers a lightweight and flexible solution. The framework features a robust tensor library, an auto-differentiation engine, a comprehensive neural network module, and advanced optimization algorithms. With built-in visualization tools and a user-friendly interface, NeuroScribe simplifies both beginner and advanced workflows. Its cross-platform compatibility, supported by CUDA and Metal Performance Shaders (MPS), ensures optimal performance, and in some scenarios, NeuroScribe demonstrates superior speed compared to leading frameworks. Additionally, NeuroScribe introduces unique libraries and features not found in other frameworks, further enhancing its versatility and appeal. The modular architecture and automatic system detection further enhance its adaptability, making NeuroScribe a versatile and powerful tool for deep learning practitioners.
Neural Information Processing ; 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II
The 228 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. The 116 papers of the first volume are organized in topical sections on computational neuroscience, learning and memory, neural network models ,supervised /unsupervised/reinforcement learning, statistical learning algorithms, optimization algorithms, novel algorithms, as well as motor control and vision. The second volume contains 112 contributions related to statistical and pattern recognition algorithms, neuromorphic hardware and implementations, robotics, data mining and knowledge discovery, real world applications, cognitive and hybrid intelligent systems, bioinformatics, neuroinformatics, brain-conputer interfaces, and novel approaches.
Neural Information Processing ; 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I
The 228 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. The 116 papers of the first volume are organized in topical sections on computational neuroscience, learning and memory, neural network models, supervised/unsupervised/reinforcement learning, statistical learning algorithms, optimization algorithms, novel algorithms, as well as motor control and vision. The second volume contains 112 contributions related to statistical and pattern recognition algorithms, neuromorphic hardware and implementations, robotics, data mining and knowledge discovery, real world applications, cognitive and hybrid intelligent systems, bioinformatics, neuroinformatics, brain-conputer interfaces, and novel approaches.
Multi-objective Management in Freight Logistics : Increasing Capacity, Service Level and Safety with Optimization Algorithms
Multi-objective Management in Freight Logistics provides decision makers with new methods and tools to implement multi-objective optimization models in logistics. The book combines theoretical aspects with applications, showing the advantages and the drawbacks of adopting scalarization techniques, and when it is worthwhile to reduce the problem to a goal programming one. The book also shows applications where more than one decision maker evaluates the effectiveness of the logistic system and thus a multilevel programming approach is sought to attain meaningful solutions.
Motion Planning in Medicine : Optimization and Simulation Algorithms for Image-Guided Procedures
The monograph written by Ron Alterovitz and Ken Goldberg combines ideas from robotics, physically-based modeling, and operations research to develop new motion planning and optimization algorithms for image-guided medical procedures. A challenge clinicians commonly face is compensating for errors caused by soft tissue deformations that occur when imaging devices or surgical tools physically contact soft tissue. A number of methods are presented which can be applied to a variety of medical procedures, from biopsies to anaesthesia injections to radiation cancer treatment. They can also be extended to address problems outside the context of medical robotics, including nonholonomic motion planning for mobile robots in field or manufacturing environments.
Metaheuristic Optimization Algorithms in Civil Engineering : New Applications
Discusses the application of metaheuristic algorithms in a number of important optimization problems in civil engineering. Advances in civil engineering technologies require greater accuracy, efficiency and speed in terms of the analysis and design of the corresponding systems. As such, it is not surprising that novel methods have been developed for the optimal design of real-world systems and models with complex configurations and large numbers of elements.
Handbook of Optimization in Telecommunications
The Handbook of Optimization in Telecommunications includes planning and design of telecommunication networks, routing, network protection, grooming, restoration, wireless communications, network location and assignment problems, Internet protocol, World Wide Web, and stochastic issues in telecommunications. The editors’ objective is to provide a reference tool for the increasing number of scientists and engineers in telecommunications who depend upon optimization in some way. Each chapter in the handbook is of an expository nature, but of scholarly treatment, and includes a brief overview of the state-of-the-art thinking relative to the topic, as well as pointers to the key references in the field. Specialists as well as nonspecialists should find this handbook stimulating and helpful.
Graph theory, combinatorics and algorithms: interdisciplinary applications
focuses on discrete mathematics and combinatorial algorithms interacting with real world problems in computer science, operations research, applied mathematics and engineering. The book contains eleven chapters written by experts in their respective fields, and covers a wide spectrum of high-interest problems across these discipline domains.The chapters focus on "real world" applications, all of which will be of considerable interest across the areas of Operations Research, Computer Science, Applied Mathematics, and Engineering. These problems include Internet congestion control, high-speed communication networks, multi-object auctions, resource allocation, software testing, data structures, etc. In sum, this is a book focused on major, contemporary problems, written by the top research scholars in the field, using cutting-edge mathematical and computational techniques.
Fast, Efficient and Predictable Memory Accesses : Optimization Algorithms for Memory Architecture Aware Compilation
Fast, Efficient and Predictable Memory Accesses presents techniques for designing fast, energy-efficient and timing predictable memory systems. By using a careful combination of compiler optimizations and architectural improvements, we can achieve more than what would be feasible at one of the levels in isolation. The described optimization algorithms achieve the goals of high performance and low energy consumption. In addition to these benefits, the use of scratchpad memories significantly improves the timing predictability of the entire system, leading to tighter worst case execution time bounds (WCET). The WCET is a relevant design parameter for all timing critical systems. In addition, the book covers algorithms to exploit the power down modes of main memories in SDRAM technology, as well as the execute-in-place feature of Flash memories. The final chapter considers the impact of the register file, which is also part of the memory hierarchy.
Expert Clouds and Applications ; Proceedings of ICOECA 2021
Features original papers from International Conference on Expert Clouds and Applications (ICOECA 2021), organized by GITAM School of Technology, Bangalore, India during February 18–19, 2021. It covers new research insights on artificial intelligence, big data, cloud computing, sustainability, and knowledge-based expert systems. The book discusses innovative research from all aspects including theoretical, practical, and experimental domains that pertain to the expert systems, sustainable clouds, and artificial intelligence technologies.
Evolutionary computation in combinatorial optimization ; 7th European Conference, EvoCOP 2007, Valencia, Spain, April 11-13, 2007, Proceedings
This book cover evolutionary algorithms as well as various other metaheuristics, like scatter search, tabu search, memetic algorithms, variable neighborhood search, greedy randomized adaptive search procedures, ant colony optimization, and particle swarm optimization algorithms. The papers are specifically dedicat.
Effective Resource Management in Manufacturing Systems : Optimization Algorithms for Production Planning
Effective Resource Management in Manufacturing Systems aims to provide robust methods for achieving effective resource allocation and to solve related problems that occur daily and often generate cost overruns, specifically focusing on problems like resource levelling, sizing of machines and production layouts, cost optimization in production planning and scheduling. This approach is based on providing quantitative methods, covering both mathematical programming and algorithms, leading to high quality solutions for the analysed problems. Details of extensive experimentation is provided for the proposed techniques to put them in a practical perspective, so that, on the one hand, the reader can reproduce them, and, on the other hand, it appears clear how they can be implemented in real scenarios.
Differential Evolution ; Vol.5 : In Search of Solutions
The human being aspires to the best possible performance. Both individuals and enterprises are looking for optimal—in other words, the best possible—solutions for situations or problems they face. Most of these problems can be expressed in mathematical terms, and so the methods of optimization undoubtedly render a significant aid. In cases where there are many local optima; intricate constraints; mixed-type variables; or noisy, time-dependent or otherwise ill-defined functions, the usual methods don’t give satisfactory results. Are you seeking fresh ideas or more efficient methods, or do you perhaps want to be well-informed about the latest achievements in optimization? If so, this book is for you. This book develops a unified insight on population-based optimization through Differential Evolution, one of the most recent and efficient optimization algorithms. You will find, in this book, everything concerning Differential Evolution and its application in its newest formulation.
Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Presents a comprehensive comparison of the performance of stochastic optimization algorithms / Includes an introduction to benchmarking and statistical analysis / Provides a web-based tool for making statistical comparisons of optimization algorithms / Overviews of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios. The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches.
Conception optimale de structures = Optimal structural design
Optimal Structural Design deals with all aspects of shape optimization, parametric, geometric and topological, and gives a large place to numerical algorithms, gradient methods and stochastic methods (with an original contribution by Marc Schoenauer for this last point). In particular, most of the structural optimization algorithms have been implemented in the FreeFem ++ finite element software and the programs are freely available on the web. Optimal structural design is devoted to structural or shape optimization and is intended for a mixed audience of applied mathematicians and mechanicians. It discusses parametric, geometric and topology optimization and gives deterministic and stochastic numerical algorithms (implemented in the FreeFem ++ finite element software).
Combinatorial Optimization in Communication Networks
Combinatorial optimization algorithms are used in many applications including the design, management, and operations of communication networks. The objective of this book is to advance and promote the theory and applications of combinatorial optimization in communication networks. The book collects a distinguished set of papers on subjects such as wireless communication systems, satellite networks, optical networks, and ad hoc networks. The topics covered range from topology control, routing optimization, and resource allocation to QoS provisioning. It is the first book that integrates rich theory from operations research with cutting-edge research in communication networks.
Laplacian Eigenvectors of Graphs : Perron-Frobenius and Faber-Krahn Type Theorems
Eigenvectors of graph Laplacians have not, to date, been the subject of expository articles and thus they may seem a surprising topic for a book. The authors propose two motivations for this new LNM volume: (1) There are fascinating subtle differences between the properties of solutions of Schrödinger equations on manifolds on the one hand, and their discrete analogs on graphs. (2) "Geometric" properties of (cost) functions defined on the vertex sets of graphs are of practical interest for heuristic optimization algorithms. The observation that the cost functions of quite a few of the well-studied combinatorial optimization problems are eigenvectors of associated graph Laplacians has prompted the investigation of such eigenvectors.
Cellular Genetic Algorithms
CELLULAR GENETIC ALGORITHMS defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book. This class of genetic algorithms is shown to produce impressive results on a whole range of domains, including complex problems that are epistatic, multi-modal, deceptive, discrete, continuous, multi-objective, and random in nature. The focus of this book is twofold. On the one hand, the authors present new algorithmic models and extensions to the basic class of Cellular GAs in order to tackle complex problems more efficiently. On the other hand, practical real world tasks are successfully faced by applying Cellular GA methodologies to produce workable solutions of real-world applications.
