Advanced Optimization With Matlab Using Big Data Techniques

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Advanced Optimization With Matlab Using Big Data Techniques

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Author : J Lopez
language : en
Publisher:
Release Date : 2019-07-07

Advanced Optimization With Matlab Using Big Data Techniques written by J Lopez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-07 with categories.

Global Optimization Toolbox provides functions that search for global solutions to problems that contain multiple maxima or minima. Toolbox solvers include surrogate, pattern search, genetic algorithm, particle swarm, simulated annealing, multi start, and global search. You can use these solvers for optimization problems where the objective or constraint function is continuous, discontinuous, stochastic, does not possess derivatives, or includes simulations or black-box functions. For problems with multiple objectives, you can identify a Pareto front using genetic algorithm or pattern search solvers. You can improve solver effective es by adjusting options and, for applicable solvers, customizing creation, update, and search functions. You can use custom data types with the genetic algorithm and simulated annealing solvers to represent problems not easily expressed with standard data types. The hybrid function option lets you improve a solution by applying a second solver after the first.Simulated annealing is a method for solving unconstrained and bound-constrained optimization problems. The method models the physical process of heating a material and then slowly lowering the temperature to decrease defects, thus minimizing the system energy. At each iteration of the simulated annealing algorithm, a new point is randomly generated. The distance of the new point from the current point, or the extent of the search, is based on a probability distribution with a scale proportional to the temperature. The algorithm accepts all new points that lower the objective, but also, with a certain probability, points that raise the objective. By accepting points that raise the objective, the algorithm avoids being trapped in local minima, and is able to explore globally for more possible solutions. An annealing schedule is selected to systematically decrease the temperature as the algorithm proceeds. As the temperature decreases, the algorithm reduces the extent of its search to converge to a minimum.You might need to formulate problems with more than one objective, since a single objective with several constraints may not adequately represent the problem being faced. If so, there is a vector of objectives, F(x) = [F1(x), F2(x), ..., Fm(x)], that must be traded off in some way. The relative importance of these objectives is not generally known until the system's best capabilities are determined and tradeoffs between the objectives fully understood. As the number of objectives increases, tradeoffs are likely to become complex and less easily quantified. The designer must rely on his or her intuition and ability to express preferences throughout the optimization cycle. Thus, requirements for a multiobjective design strategy must enable a natural problema formulation to be expressed, and be able to solve the problem and enter preferences into a numerically tractable and realistic design proble

Advanced Optimization Techniques And Examples With Matlab

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Author : E. Clapton
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-11-12

Advanced Optimization Techniques And Examples With Matlab written by E. Clapton and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-12 with categories.

MATLAB Optimization Toolbox provides widely used algorithms for and large-scale optimization. These algorithms solve constrained and unconstrained continuous and discrete problems. The toolbox, developed in this book, includes functions for linear programming, quadratic programming, binary integer programming, nonlinear optimization, nonlinear least squares, systems of nonlinear equations, and multiobjective optimization. You can use them to find optimal solutions, perform tradeoff analyses, balance multiple design alternatives, and incorporate optimization methods into algorithms and models.The more important features are the next:* Interactive tools for defining and solving optimization problems and monitoring solution progress* Solvers for nonlinear and multiobjective optimization * Solvers for nonlinear least squares, data fitting, and nonlinear equations* Methods for solving quadratic and linear programming problems * Methods for solving binary integer programming problems* Parallel computing support in selected constrained nonlinear solvers

Data Mining Big Data Analytics And Machine Learning With Neural Networks Using Matlab

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Author : C Perez
language : en
Publisher: Independently Published
Release Date : 2019-05-23

Data Mining Big Data Analytics And Machine Learning With Neural Networks Using Matlab written by C Perez and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-23 with categories.

Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today's technology, it's possible to analyze your data and get answers from it almost immediately - an effort that's slower and less efficient with more traditional business intelligence solutions.The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term "big data," businesses were using basic analytics (essentially numbers in a spreadsheet that were manually examined) to uncover insights and trends.Data Mining can be defined as a process of discovering new and significant relationships, patterns and trends when examining large amounts of data. The techniques of Data Mining pursue the automatic discovery of the knowledge contained in the information stored in an orderly manner in large databases. These techniques aim to discover patterns, profiles and trends through the analysis of data using advanced statistical techniques of multivariate data analysis.The goal is to allow the researcher-analyst to find a useful solution to the problem raised through a better understanding of the existing data.Data Mining uses two types of techniques: predictive techniques, which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques, which finds hidden patterns or intrinsic structures in input data.

Data Mining And Big Data Analytics With Neural Networks Using Matlab

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Author : C Perez
language : en
Publisher: Independently Published
Release Date : 2019-05-22

Data Mining And Big Data Analytics With Neural Networks Using Matlab written by C Perez and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-22 with categories.

The availability of large volumes of data (Big Data) and the generalized use of computer tools has transformed research and data analysis, orienting it towards certain specialized techniques encompassed under the generic name of Analytics (Big Data Analytics) that includes Multivariate Data Analysis (MDA), Data Mining and other Business Intelligence techniques.Data Mining can be defined as a process of discovering new and significant relationships, patterns and trends when examining large amounts of data. The techniques of Data Mining pursue the automatic discovery of the knowledge contained in the information stored in an orderly manner in large databases. These techniques aim to discover patterns, profiles and trends through the analysis of data using advanced statistical techniques of multivariate data analysis.The goal is to allow the researcher-analyst to find a useful solution to the problem raised through a better understanding of the existing data.Data Mining uses two types of techniques: predictive techniques, which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques, which finds hidden patterns or intrinsic structures in input data.

Advanced Optimization Functions In Matlab

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Author : J Lopez
language : en
Publisher:
Release Date : 2019-07-08

Advanced Optimization Functions In Matlab written by J Lopez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-08 with categories.

Global Optimization Toolbox provides functions that search for global solutions to problems that contain multiple maxima or minima. Toolbox solvers include surrogate, pattern search, genetic algorithm, particle swarm, simulated annealing, multi start, and global search. You can use these solvers for optimization problems where the objective or constraint function is continuous, discontinuous, stochastic, does not possess derivatives, or includes simulations or black-box functions. For problems with multiple objectives, you can identify a Pareto front using genetic algorithm or pattern search solvers. You can improve solver effective es by adjusting options and, for applicable solvers, customizing creation, update, and search functions. You can use custom data types with the genetic algorithm and simulated annealing solvers to represent problems not easily expressed with standard data types. The hybrid function option lets you improve a solution by applying a second solver after the first.Global Optimization Toolbox functions include three direct search algorithms called the generalized pattern search (GPS) algorithm, the generating set search (GSS) algorithm, and the mesh adaptive search (MADS) algorithm. All are pattern search algorithms that compute a sequence of points that approach an optimal point. At each step, the algorithm searches a set of points, called a mesh, around the current point-the point computed at the previous step of the algorithm. The mesh is formed by adding the current point to a scalar multiple of a set of vectors called a pattern. If the pattern search algorithm finds a point in the mesh that improves the objective function at the current point, the new point becomes the current point at the next step of the algorithm.The GPS algorithm uses fixed direction vectors. The GSS algorithm is identical to the GPS algorithm, except when there are linear constraints, and when the current point is near a linear constraint boundary. The MADS algorithm uses a random selection of vectors to define the mesh.A surrogate is a function that approximates an objective function. The surrogate is useful because it takes little time to evaluate.Multiobjective optimization is concerned with the minimization of a vector of objectives F(x) that can be the subject of a number of constraints or bounds.In Big Data problems Parallel Processing is an attractive way to speed optimization algorithms. To use parallel processing, you must have a Parallel Computing Toolbox license, and have a parallel worker pool (parpool).This book develops the advanced functions of Matlab for optimization through examples

Advanced Optimization With Matlab

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Author : J Lopez
language : en
Publisher:
Release Date : 2019-06-18

Advanced Optimization With Matlab written by J Lopez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-18 with categories.

Optimization Toolbox provides functions for finding parameters that minimize or maximize objectives while satisfying constraints. The toolbox includes solvers for linear programming (LP), mixed-integer linear programming (MILP), quadratic programming(QP), nonlinear programming (NLP), constrained linear least squares, nonlinear least squares, and nonlinear equations. You can define your optimization problem with functions and matrices or by specifying variable expressions that reflect the underlying mathematics. You can use the toolbox solvers to fin optimal solutions to continuous and discrete problems, perform trade of analyses, and incorporate optimization methods into algorithms and applications. The toolbox lets you perform design optimization tasks, including parameter estimation, component selection, and parameter tuning. It can be used to fin optimal solutions in applications such as portfolio optimization, resource allocation, and production planning and scheduling.Global Optimization Toolbox provides functions that search for global solutions to problems that contain multiple maxima or minima. Toolbox solvers include surrogate, pattern search, genetic algorithm, particle swarm, simulated annealing, multi start, and global search. You can use these solvers for optimization problems where the objective or constraint function is continuous, discontinuous, stochastic, does not possess derivatives, or includes simulations or black-box functions. For problems with multiple objectives, you can identify a Pareto front using genetic algorithm or pattern search solvers. You can improve solver effective es by adjusting options and, for applicable solvers, customizing creation, update, and search functions. You can use custom data types with the genetic algorithm and simulated annealing solvers to represent problems not easily expressed with standard data types. The hybrid function option lets you improve a solution by applying a second solver after the first.

Mastering Matlab

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Author : Cybellium Ltd
language : en
Publisher: Cybellium Ltd
Release Date : 2023-09-26

Mastering Matlab written by Cybellium Ltd and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-26 with Computers categories.

Are you ready to revolutionize your approach to scientific computing and data analysis? "Mastering MATLAB" is your ultimate guide to harnessing the full potential of the MATLAB programming language. Whether you're a seasoned researcher seeking to enhance your capabilities or a data enthusiast ready to dive into the world of numerical analysis, this comprehensive guide will equip you with the knowledge and tools to develop sophisticated solutions for complex problems. Key Features: 1. Comprehensive Exploration of MATLAB Fundamentals: Immerse yourself in the core concepts of MATLAB programming, from its intuitive syntax to its versatile features. Build a strong foundation that empowers you to tackle intricate computational challenges with ease. 2. Data Analysis and Visualization Excellence: Dive into MATLAB's capabilities for data analysis and visualization. Learn how to manipulate and analyze data, create insightful visualizations, and extract meaningful insights from complex datasets. 3. Numerical Computing and Simulation: Uncover MATLAB's power in numerical computing and simulation. Explore techniques for solving mathematical problems, performing simulations, and optimizing algorithms to deliver accurate and efficient results. 4. Advanced Mathematics and Engineering Applications: Push the boundaries of your MATLAB knowledge by exploring advanced mathematical and engineering applications. From linear algebra to differential equations, learn how to apply MATLAB's tools to solve real-world problems. 5. Image and Signal Processing: Discover MATLAB's capabilities in image and signal processing. Master techniques for image enhancement, filtering, and feature extraction, and explore signal processing tools that enable you to work with audio and other time-based data. 6. Machine Learning and Data Science: Navigate the world of machine learning and data science using MATLAB. Learn how to implement algorithms, build predictive models, and perform classification and regression tasks with ease. 7. Optimization and Algorithm Development: Dive into MATLAB's optimization toolbox and learn how to design and implement efficient algorithms. Explore techniques for solving linear and nonlinear optimization problems to achieve optimal solutions. 8. Graphical User Interfaces (GUIs): Discover how to create interactive and user-friendly graphical user interfaces using MATLAB's App Designer. Build custom GUIs that enable users to interact with your applications intuitively. 9. Deployment and Integration: Navigate the process of deploying MATLAB applications to various platforms. Learn about integration with other programming languages, tools, and libraries, and explore techniques for sharing your work with a wider audience. Who This Book Is For: "Mastering MATLAB" is a must-have resource for professionals, researchers, students, and enthusiasts who are eager to harness the capabilities of the MATLAB programming language. Whether you're a novice or an experienced programmer seeking to explore MATLAB's vast capabilities, this book will guide you through the language's intricacies and empower you to develop advanced solutions.

Operations Research Optimization With Matlab Multiobjective Quadratic And Mixed Programming

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Author : Perez C.
language : en
Publisher:
Release Date : 2017-08-16

Operations Research Optimization With Matlab Multiobjective Quadratic And Mixed Programming written by Perez C. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-16 with categories.

The generalization of optimization theory and techniques to other formulations comprises a large area of applied mathematics. Optimization includes finding "best available" values of some objective function given a defined domain (or input), including a variety of different types of objective functions and different types of domains.Adding more than one objective to an optimization problem adds complexity. For example, to optimize a structural design, one would desire a design that is both light and rigid. When two objectives conflict, a trade-off must be created. There may be one lightest design, one stiffest design, and an infinite number of designs that are some compromise of weight and rigidity. The set of trade-off designs that cannot be improved upon according to one criterion without hurting another criterion is known as the Pareto set. The curve created plotting weight against stiffness of the best designs is known as the Pareto frontier.A design is judged to be "Pareto optimal" (equivalently, "Pareto efficient" or in the Pareto set) if it is not dominated by any other design: If it is worse than another design in some respects and no better in any respect, then it is dominated and is not Pareto optimal. The choice among "Pareto optimal" solutions to determine the "favorite solution" is delegated to the decision maker. In other words, defining the problem as multi-objective optimization signals that some information is missing: desirable objectives are given but combinations of them are not rated relative to each other. In some cases, the missing information can be derived by interactive sessions with the decision maker.Multi-objective optimization problems have been generalized further into vector optimization problems where the (partial) ordering is no longer given by the Pareto ordering.Optimization problems are often multi-modal; that is, they possess multiple good solutions. They could all be globally good or there could be a mix of globally good and locally good solutions. Obtaining all (or at least some of) the multiple solutions is the goal of a multi-modal optimizer.Classical optimization techniques due to their iterative approach do not perform satisfactorily when they are used to obtain multiple solutions, since it is not guaranteed that different solutions will be obtained even with different starting points in multiple runs of the algorithm. Evolutionary algorithms, however, are a very popular approach to obtain multiple solutions in a multi-modal optimization task.This book develops the following topics:* "Multiobjective Optimization Algorithms" * "Using fminimax with a Simulink Model" * "Signal Processing Using fgoalattain" * "Generate and Plot a Pareto Front" * "Linear Programming Algorithms" * "Maximize Long-Term Investments Using Linear Programming" * "Mixed-Integer Linear Programming Algorithms" * "Tuning Integer Linear Programming" * "Mixed-Integer Linear Programming Basics" * "Optimal Dispatch of Power Generators" * "Mixed-Integer Quadratic Programming Portfolio Optimization" * "Quadratic Programming Algorithms"* "Quadratic Minimization with Bound Constraints" * "Quadratic Minimization with Dense, Structured Hessian"* "Large Sparse Quadratic Program with Interior Point Algorithm" * "Least-Squares (Model Fitting) Algorithms" * "lsqnonlin with a Simulink Model" * "Nonlinear Least Squares With and Without Jacobian" * "Linear Least Squares with Bound Constraints" * "Optimization App with the lsqlin Solver" * "Maximize Long-Term Investments Using Linear Programming" * "Jacobian Multiply Function with Linear Least Squares" * "Nonlinear Curve Fitting with lsqcurvefit" * "Fit a Model to Complex-Valued Data" * "Systems of Equations" * "Nonlinear Equations with Analytic Jacobian" * "Nonlinear Equations with Jacobian" * "Nonlinear Equations with Jacobian Sparsity Pattern"* "Nonlinear Systems with Constraints" * "Parallel Computing for Optimization"

Machine Learning Models And Algorithms For Big Data Classification

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Author : Shan Suthaharan
language : en
Publisher: Springer
Release Date : 2015-10-20

Machine Learning Models And Algorithms For Big Data Classification written by Shan Suthaharan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-20 with Business & Economics categories.

This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.

Deep Learning And Big Data Matlab Applications

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Author :
language : en
Publisher:
Release Date :

Deep Learning And Big Data Matlab Applications written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.