Products related to Optimization:
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Analytics and Optimization for Renewable Energy Integration
The scope of this book covers the modeling and forecast of renewable energy and operation and planning of power system with renewable energy integration.The first part presents mathematical theories of stochastic mathematics; the second presents modeling and analytic techniques for renewable energy generation; the third provides solutions on how to handle the uncertainty of renewable energy in power system operation.It includes advanced stochastic unit commitment models to acquire the optimal generation schedule under uncertainty, efficient algorithms to calculate the probabilistic power, and an efficient operation strategy for renewable power plants participating in electricity markets.
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Additive Manufacturing Technology : Design, Optimization, and Modeling
Additive Manufacturing Technology Highly comprehensive resource covering all key aspects of the current developments of additive manufacturing Additive Manufacturing Technology: Design, Optimization, and Modeling provides comprehensive and in-depth knowledge of the latest advances in various additive manufacturing technologies for polymeric materials, metals, multi-materials, functionally graded materials, and cell-laden bio-inks.It also details the application of numerical modeling in facilitating the design and optimization of materials, processes, and printed parts in additive manufacturing. The topics covered in this book include: Fundamentals and applications of 4D printing, 3D bioprinting of cell-laden bio-inks, and multi-material additive manufacturing Alloy design for metal additive manufacturing, mechanisms of metallurgical defect formation, and the mechanical properties of printed alloys Modified inherent strain method for the rapid prediction of residual stress and distortion within parts fabricated by additive manufacturing Modeling of the different stages in polymer and metal additive manufacturing processes, including powder spreading, melting, and thermal stress evolution By providing extensive coverage of highly relevant concepts and important topics in the field of additive manufacturing, this book highlights its essential role in Industry 4.0 and serves as a valuable resource for scientists, engineers, and students in materials science, engineering, and biomedicine.
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Algorithms : Big Data, Optimization Techniques, Cyber Security
Algorithms are ubiquitous in the contemporary technological world, and they ultimately consist of finite sequences of instructions used to accomplish tasks with necessary input values.This book analyses the top performing algorithms in areas as diverse as Big Data, Artificial Intelligence, Optimization Techniques and Cloud & Cyber Security Systems in order to explore their power and limitations.
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Robust Optimization
Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology.Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject.Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution.The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems.The theory is supported by numerous examples and computational illustrations.An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.
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With which symbols can efficiency and optimization be represented?
Efficiency and optimization can be represented by symbols such as a lightbulb for efficiency, a magnifying glass for optimization, and an arrow pointing upwards for improvement. These symbols visually convey the concepts of streamlining processes, maximizing output, and enhancing performance. They are commonly used in presentations, infographics, and diagrams to communicate the importance of efficiency and optimization in various contexts.
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Which symbols can be used to represent efficiency and optimization?
Symbols such as a light bulb, a gear, or an arrow pointing upwards can be used to represent efficiency and optimization. A light bulb symbolizes bright ideas and innovation, while a gear represents smooth functioning and productivity. An arrow pointing upwards can symbolize progress and improvement in efficiency. These symbols are commonly used in visual communication to convey the concept of efficiency and optimization.
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What is price optimization?
Price optimization is the process of using data and analytics to determine the most effective pricing strategy for a product or service. It involves analyzing factors such as market demand, competitor pricing, and customer behavior to set prices that maximize revenue and profit. By continuously adjusting prices based on these insights, businesses can find the optimal balance between attracting customers and maximizing profitability. Price optimization helps businesses stay competitive in the market and adapt to changing conditions to achieve their financial goals.
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What is linear optimization?
Linear optimization, also known as linear programming, is a mathematical method for determining the best outcome in a given mathematical model for a given set of requirements. It involves maximizing or minimizing a linear objective function, subject to a set of linear equality and inequality constraints. Linear optimization is widely used in various fields such as economics, engineering, and business to optimize resource allocation, production planning, and decision-making processes. It provides a systematic and efficient approach to solving complex problems with multiple variables and constraints.
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Convex Optimization
Convex optimization problems arise frequently in many different fields.This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency.The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems.Duality and approximation techniques are then covered, as are statistical estimation techniques.Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods.The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them.It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.
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Optimization Algorithms
Solve design, planning, and control problems using modern machine learning and AI techniques. In Optimization Algorithms: AI techniques for design, planning, and control problems you will learn: Machine learning methods for search and optimization problemsThe core concepts of search and optimizationDeterministic and stochastic optimization techniquesGraph search algorithmsNature-inspired search and optimization algorithmsEfficient trade-offs between search space exploration and exploitationState-of-the-art Python libraries for search and optimization About the technology: Search and optimization algorithms are powerful tools that can help practitioners find optimal or near-optimal solutions to a wide range of design, planning and control problems.When you open a route planning app, call for a rideshare, or schedule a hospital appointment, an AI algorithm works behind the scenes to make sure you get an optimized result.This guide reveals the classical and modern algorithms behind these services.
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Numerical Optimization
Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization.It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout.There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience.It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business.It also serves as a handbook for researchers and practitioners in the field.The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. There is a selected solutions manual for instructors for the new edition.
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Bayesian Optimization
Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond.This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way.This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations.The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies.Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applications.
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What is upstream optimization?
Upstream optimization refers to the process of identifying and addressing issues at the beginning of a production or supply chain to improve overall efficiency and effectiveness. By focusing on optimizing processes, materials, and resources at the upstream stage, companies can prevent problems from occurring downstream, leading to cost savings, improved quality, and increased productivity. This proactive approach helps organizations streamline their operations and enhance their competitive advantage in the market.
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Is map optimization harmful?
No, map optimization is not harmful. In fact, it is beneficial for improving the user experience, reducing load times, and increasing website performance. Properly optimizing maps can help websites load faster and provide a better overall experience for users.
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What is mathematical optimization?
Mathematical optimization is the process of finding the best solution to a problem from a set of possible solutions. It involves maximizing or minimizing a certain objective function while satisfying a set of constraints. This can be applied to a wide range of fields, including engineering, economics, and computer science, to help make better decisions and improve efficiency. Optimization problems can be solved using various mathematical techniques such as linear programming, nonlinear programming, and integer programming.
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What is optimization 3?
Optimization 3 refers to the process of maximizing or improving the efficiency and effectiveness of a system, process, or strategy. It involves identifying areas for improvement, setting specific goals, and implementing changes to achieve the best possible outcome. Optimization 3 often involves using data analysis, mathematical modeling, and advanced algorithms to make informed decisions and drive continuous improvement. This approach is commonly used in various fields such as engineering, business, and technology to enhance performance and achieve better results.
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