Products related to Optimization:
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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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Optimization for Data Analysis
Optimization techniques are at the core of data science, including data analysis and machine learning.An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas.This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science.An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems.Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundations of the analysis of nonsmooth functions and optimization duality; and the back-propagation approach, relevant to neural networks.
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Efficient Go : Data-Driven Performance Optimization
With technological advancements, fast markets, and higher complexity of systems, software engineers tend to skip the uncomfortable topic of software efficiency.However, tactical, observability-driven performance optimizations are vital for every product to save money and ensure business success. With this book, any engineer can learn how to approach software efficiency effectively, professionally, and without stress.Author Bartlomiej Plotka provides the tools and knowledge required to make your systems faster and less resource-hungry.Efficient Go guides you in achieving better day-to-day efficiency using Go.In addition, most content is language-agnostic, allowing you to bring small but effective habits to your programming or product management cycles. This book shows you how to: Clarify and negotiate efficiency goalsOptimize efficiency on various levelsUse common resources like CPU and memory effectivelyAssess efficiency using observability signals like metrics, logging, tracing, and (continuous) profiling via open source projects like Prometheus, Jaeger, and ParcaApply tools like go test, pprof, benchstat, and k6 to create reliable micro and macro benchmarksEfficiently use Go and its features like slices, generics, goroutines, allocation semantics, garbage collection, and more!
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Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making : Optimization Applications
This book comprehensively discusses nature-inspired algorithms, deep learning methods, applications of mathematical programming, and artificial intelligence techniques.It further covers important topics such as the use of machine learning and the Internet of Things and multi-objective optimization under Fermatean hesitant fuzzy and uncertain environment. This book:Addresses solving practical problems such as supply chain management, smart manufacturing, and healthcare analytics using intelligent computing and discusses solving the fuzzy inference system in ant colony optimization for traveling salesman problemPresents an overview of artificial intelligence (AI) and explainable AI decision-making (XAIDM) and illustrates a data-driven optimization concept for modeling environmental and economic sustainabilityDiscusses machine learning-based multi-objective optimization technique for load balancing in integrated fog-cloud environmentExplains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goalsDiscusses sustainable transit of hazardous waste, green fractional transportation system, perishable inventory, M-estimation of functional regression operator, and intuitionistic fuzzy sets applicationsThe text is primarily written for graduate students and academic researchers in diverse fields, including operations research, mathematics, statistics, computer science, information and communication technology, and industrial engineering.
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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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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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Bio-Inspired Optimization for Medical Data Mining
This book is a comprehensive exploration of bio-inspired optimization techniques and their potential applications in healthcare.Bio-Inspired Optimization for Medical Data Mining is a groundbreaking book that delves into the convergence of nature’s ingenious algorithms and cutting-edge healthcare technology.Through a comprehensive exploration of state-of-the-art algorithms and practical case studies, readers gain unparalleled insights into optimizing medical data processing, enabling more precise diagnosis, optimizing treatment plans, and ultimately advancing the field of healthcare. Organized into 15 chapters, readers learn about the theoretical foundation of pragmatic implementation strategies and actionable advice.In addition, it addresses current developments in molecular subtyping and how they can enhance clinical care.By bridging the gap between cutting-edge technology and critical healthcare challenges, this book is a pivotal contribution, providing a roadmap for leveraging nature-inspired algorithms.In this book, the reader will discover Cutting-edge bio-inspired algorithms designed to optimize medical data processing, providing efficient and accurate solutions for complex healthcare challenges;How bio-inspired optimization can fine-tune diagnostic accuracy, leading to better patient outcomes and improved medical decision-making;How bio-inspired optimization propels healthcare into a new era, unlocking transformative solutions for medical data analysis;Practical insights and actionable advice on implementing bio-inspired optimization techniques and equipping effective real-world medical data scenarios;Compelling case studies illustrating how bio-inspired optimization has made a significant impact in the medical field, inspiring similar success stories. Audience This book is designed for a wide-ranging audience, including medical professionals, healthcare researchers, data scientists, and technology enthusiasts.
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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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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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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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