Products related to Optimization:
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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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Why the Church? : Self-Optimization or Community of Faith
Why did Christianity produce the special organizational form "church" in the first place?Is it possible to be a Christian without the church?To what extent is Christian faith in community with other believers an alternative to the mere self-optimization of individuals?In this accessible and questioning new work, Hans Joas traverses theological, church-historical, sociological, and ethical territory in search of a viable conception of the church adequate to contemporary globalized societies.Across eleven essays that draw on work by Ernst Troeltsch, Max Weber, Emile Durkheim, H.Richard Niebuhr, Leszek Kolakowski and others, Joas reflects on key debates—from the failure of so-called secularization theory to explain religiosity in modern society, to the role of Christianity and the church in relation to rampant nationalism and refugee crises, and to the question of whether or not human dignity ever was, or still is, the highest value in the West.Addressing the sociology of the church as the distinctive communal formation of Christianity for the last two millennia, Joas underscores the need for Christian conceptions of church to balance theological sensibility with concrete sociological grounding.In the process, he considers the relation of a community of faith to contemporary ideas about the optimization of life.
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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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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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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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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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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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Building Rural Community Resilience Through Innovation and Entrepreneurship
Drawing from empirical analyses, case studies, and a synthesis of best practices, this book explores how innovation manifests itself in rural places and how it contributes to entrepreneurial development and resilience.Innovation in rural places may come about as a result of new forms of collaboration; policies that leverage rural assets and address critical service or product gaps; novel strategies for accessing financial capital; infusion of arts into aspects of community life; and cultivation of networks that bridge entrepreneurs, organizations, and institutions.The chapters illustrate how a number of innovation-related characteristics relate to economic vibrancy in rural places such as a strong connection to the arts, adaptive and sustainable use of natural resources, value-chain integrated food systems, robust bridging social capital networks, creative leveraging of technology, and presence of innovation-focused entrepreneurs.Through exploration of these and other topics, this book will provide insights and best practices for rural community and economic development scholars and practitioners seeking to strengthen the rural innovation ecosystem.
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Algorithms for Optimization
A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms.The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints.Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics.Figures, examples, and exercises convey the intuition behind the mathematical approaches.The text provides concrete implementations in the Julia programming language.Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization.Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text.The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.
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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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What does self-optimization mean?
Self-optimization refers to the process of improving oneself in various aspects such as physical, mental, emotional, and spiritual well-being. It involves setting personal goals, identifying areas for improvement, and taking deliberate actions to enhance one's overall quality of life. Self-optimization also involves self-reflection, self-awareness, and continuous learning to achieve personal growth and fulfillment. It is a proactive approach to self-improvement and self-care, aiming to maximize one's potential and live a more balanced and fulfilling life.
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Can you explain 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. The objective function represents the quantity to be optimized, while the constraints represent the limitations or restrictions on the decision variables. Linear optimization is widely used in various fields such as economics, engineering, and business to make efficient use of resources and to optimize decision-making processes.
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