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Optimization of Tuned Mass Dampers: Using Active and Passive Control (Studies in Systems, Decision and Control #432)
by Gebrail Bekdaş Sinan Melih NigdeliThis book is a timely book to summarize the latest developments in the optimization of tuned mass dampers covering all classical approaches and new trends including metaheuristic algorithms. Also, artificial intelligence and machine learning methods are included to predict optimum results by skipping long optimization processes. Another difference and advantage of the book are to provide chapters about several types of control types including passive tuned mass dampers, active tuned mass dampers, tuned liquid dampers, tuned liquid column dampers and inerter dampers. Tuned mass dampers (TMDs) are vibration absorber devices used in all types of mechanic systems. The key factor in the design is an effective tuning of TMDs for the desired performance. In practice, several high-rise structures and bridges were designed by including TMDs. Also, TMDs were installed after the construction of the structures after several negative experiences resulting from the disturbing sway of the structures. In optimum design, several closed-form expressions have been proposed for optimum frequency and damping ratio of TMDs, but the exact optimization requires iterative optimization approaches. The current trend is to use evolutionary algorithms and metaheuristic optimization methods to reach the goal.
Optimization Problems and Their Applications: 7th International Conference, OPTA 2018, Omsk, Russia, July 8-14, 2018, Revised Selected Papers (Communications in Computer and Information Science #871)
by Anton Eremeev Michael Khachay Yury Kochetov Panos PardalosThis book constitutes extended, revised and selected papers from the 7th International Conference on Optimization Problems and Their Applications, OPTA 2018, held in Omsk, Russia in July 2018. The 27 papers presented in this volume were carefully reviewed and selected from a total of 73 submissions. The papers are listed in thematic sections, namely location problems, scheduling and routing problems, optimization problems in data analysis, mathematical programming, game theory and economical applications, applied optimization problems and metaheuristics.
Optimization Problems in Graph Theory: In Honor of Gregory Z. Gutin's 60th Birthday (Springer Optimization and Its Applications #139)
by Boris GoldengorinThis book presents open optimization problems in graph theory and networks. Each chapter reflects developments in theory and applications based on Gregory Gutin’s fundamental contributions to advanced methods and techniques in combinatorial optimization. Researchers, students, and engineers in computer science, big data, applied mathematics, operations research, algorithm design, artificial intelligence, software engineering, data analysis, industrial and systems engineering will benefit from the state-of-the-art results presented in modern graph theory and its applications to the design of efficient algorithms for optimization problems. Topics covered in this work include:· Algorithmic aspects of problems with disjoint cycles in graphs· Graphs where maximal cliques and stable sets intersect· The maximum independent set problem with special classes· A general technique for heuristic algorithms for optimization problems · The network design problem with cut constraints· Algorithms for computing the frustration index of a signed graph· A heuristic approach for studying the patrol problem on a graph· Minimum possible sum and product of the proper connection number· Structural and algorithmic results on branchings in digraphs · Improved upper bounds for Korkel--Ghosh benchmark SPLP instances
Optimization, Simulation and Control: ICOSC 2022, Ulaanbaatar, Mongolia, June 20–22 (Springer Proceedings in Mathematics & Statistics #434)
by Rentsen Enkhbat Altannar Chinchuluun Panos M. PardalosThis volume gathers selected, peer-reviewed works presented at the 7th International Conference on Optimization, Simulation and Control, ICOSC 2022, held at the National University of Mongolia, Ulaanbaatar, June 20–22, 2022. Topics covered include (but are not limited to) mathematical programming; network, global, linear, nonlinear, parametric, stochastic, and multi-objective optimization; control theory; biomathematics; and deep and machine learning, to name a few. Held every three years since 2002, the ICOSC conference has become a traditional gathering for experienced and young researchers in optimization and control to share recent findings in these fields and discuss novel applications in myriad sectors. Researchers and graduate students in the fields of mathematics, engineering, and computer science can greatly benefit from this book, which can also be enjoyed by advanced practitioners in research laboratories and the industry. The 2022 edition of the ICOSC conference was sponsored by the Mongolian Academy of Sciences, the National University of Mongolia and the German-Mongolian Institute for Resources and Technology.
Optimization Strategies: A Decade of Metaheuristic Algorithm Development (Intelligent Systems Reference Library #266)
by Erik Cuevas Angel Chavarin-Fajardo Cesar Ascencio-Piña Sonia Garcia-De-LiraThis book is to explore the development of metaheuristic algorithms over the past decade, focusing on key advancements in their components and structural features, which have driven progress in search techniques. This analysis aims to provide readers with a thorough understanding of the fundamental aspects of these methods, which are essential for their practical application. To offer a broad perspective on the evolution of metaheuristic algorithms, this book reviews 11 specific algorithms developed by the evolutionary computation group at the University of Guadalajara over the past 10 years. These algorithms illustrate the most significant mechanisms and structures discussed in the academic and research communities during their development. By studying these examples, readers will gain valuable insights into the innovative methods and strategic improvements that have shaped the field. The book is designed from a teaching standpoint, making it suitable for undergraduate and postgraduate students in science, electrical engineering, or computational mathematics. Moreover, engineering practitioners unfamiliar with metaheuristic computation will appreciate how these techniques have been adapted to address complex real-world engineering problems, moving beyond theoretical constructs.
Optimization Techniques in Computer Vision
by Mongi A. Abidi Andrei V. Gribok Joonki PaikThis book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function. Unconstrained optimization problems have been intensively studied, and many algorithms and tools have been developed to solve them. Most practical optimization problems, however, arise with a set of constraints. Typical examples of constraints include: (i) pre-specified pixel intensity range, (ii) smoothness or correlation with neighboring information, (iii) existence on a certain contour of lines or curves, and (iv) given statistical or spectral characteristics of the solution. Regularized optimization is a special method used to solve a class of constrained optimization problems. The term regularization refers to the transformation of an objective function with constraints into a different objective function, automatically reflecting constraints in the unconstrained minimization process. Because of its simplicity and efficiency, regularized optimization has many application areas, such as image restoration, image reconstruction, optical flow estimation, etc. Optimization plays a major role in a wide variety of theories for image processing and computer vision. Various optimization techniques are used at different levels for these problems, and this volume summarizes and explains these techniques as applied to image processing and computer vision.
Optimization Techniques in Engineering: Advances and Applications (Sustainable Computing and Optimization)
by Anita Khosla Prasenjit Chatterjee Ikbal Ali Dheeraj JoshiOPTIMIZATION TECHNIQUES IN ENGINEERING The book describes the basic components of an optimization problem along with the formulation of design problems as mathematical programming problems using an objective function that expresses the main aim of the model, and how it is to be either minimized or maximized; subsequently, the concept of optimization and its relevance towards an optimal solution in engineering applications, is explained. This book aims to present some of the recent developments in the area of optimization theory, methods, and applications in engineering. It focuses on the metaphor of the inspired system and how to configure and apply the various algorithms. The book comprises 30 chapters and is organized into two parts: Part I — Soft Computing and Evolutionary-Based Optimization; and Part II — Decision Science and Simulation-Based Optimization, which contains application-based chapters. Readers and users will find in the book: An overview and brief background of optimization methods which are used very popularly in almost all applications of science, engineering, technology, and mathematics; An in-depth treatment of contributions to optimal learning and optimizing engineering systems; Maps out the relations between optimization and other mathematical topics and disciplines; A problem-solving approach and a large number of illustrative examples, leading to a step-by-step formulation and solving of optimization problems. Audience Researchers, industry professionals, academicians, and doctoral scholars in major domains of engineering, production, thermal, electrical, industrial, materials, design, computer engineering, and natural sciences. The book is also suitable for researchers and postgraduate students in mathematics, applied mathematics, and industrial mathematics.
Optimization, Uncertainty and Machine Learning in Wind Energy Conversion Systems (Engineering Optimization: Methods and Applications)
by Kishalay Mitra Richard Everson Jonathan FieldsendThis book presents state-of-the-art technologies in wind farm layout optimization and control to improve the current industry/research practice. The contents take readers towards a different kind of uncertainty handling through the discussion on several techniques enabling maximum energy harnessing out of uncertain situations. The book aims to give a detailed overview of such concepts in the first part, where the recent advancements in the fields of (i) Wind farm layout optimization, (ii) Multi-objective Optimization and Uncertainty handling in optimization methods, (iii) Development of Machine Learning-based surrogate models in optimization, and (iv) Different types of wake models for wind farms will be discussed. The second part will cover the application of the aforementioned techniques on the wind farm layout optimization and control through several chapters such as (i) Wind farm performance assessment using Computational Fluid Dynamics (CFD) tools, (ii) Artificial Neural Network (ANN) based hybrid wake models, (iii) Long Short-term Memory (LSTM) & Support Vector Regression (SVR) based forecasting and micro-siting, (iv) windfarm micro-siting using data-driven Robust Optimization (RO) as well as Generative Adversarial Networks (GANs), (v) Reinforcement learning (RL) based wind farm control and (vi) Application of eXplainable AI (XAI) tools for interpreting wind time-series data. In this manner, the book provides state-of-the-art techniques in the fields of multi-objective optimization, Evolutionary Algorithms, Machine Learning surrogate models, Bayesian Optimization, Data Analysis, and Optimization under Uncertainty and their applications in the field of wind energy generation that can be extremely generic and can be applied to many other engineering fields. This volume will be of interest to those in academia and industry.
Optimization Under Stochastic Uncertainty: Methods, Control and Random Search Methods (International Series in Operations Research & Management Science #296)
by Kurt MartiThis book examines application and methods to incorporating stochastic parameter variations into the optimization process to decrease expense in corrective measures. Basic types of deterministic substitute problems occurring mostly in practice involve i) minimization of the expected primary costs subject to expected recourse cost constraints (reliability constraints) and remaining deterministic constraints, e.g. box constraints, as well as ii) minimization of the expected total costs (costs of construction, design, recourse costs, etc.) subject to the remaining deterministic constraints.After an introduction into the theory of dynamic control systems with random parameters, the major control laws are described, as open-loop control, closed-loop, feedback control and open-loop feedback control, used for iterative construction of feedback controls. For approximate solution of optimization and control problems with random parameters and involving expected cost/loss-type objective, constraint functions, Taylor expansion procedures, and Homotopy methods are considered, Examples and applications to stochastic optimization of regulators are given. Moreover, for reliability-based analysis and optimal design problems, corresponding optimization-based limit state functions are constructed. Because of the complexity of concrete optimization/control problems and their lack of the mathematical regularity as required of Mathematical Programming (MP) techniques, other optimization techniques, like random search methods (RSM) became increasingly important.Basic results on the convergence and convergence rates of random search methods are presented. Moreover, for the improvement of the – sometimes very low – convergence rate of RSM, search methods based on optimal stochastic decision processes are presented. In order to improve the convergence behavior of RSM, the random search procedure is embedded into a stochastic decision process for an optimal control of the probability distributions of the search variates (mutation random variables).
Optimized C++: Proven Techniques for Heightened Performance
by Kurt GuntherothIn today’s fast and competitive world, a program’s performance is just as important to customers as the features it provides. This practical guide teaches developers performance-tuning principles that enable optimization in C++. You’ll learn how to make code that already embodies best practices of C++ design run faster and consume fewer resources on any computer—whether it’s a watch, phone, workstation, supercomputer, or globe-spanning network of servers.Author Kurt Guntheroth provides several running examples that demonstrate how to apply these principles incrementally to improve existing code so it meets customer requirements for responsiveness and throughput. The advice in this book will prove itself the first time you hear a colleague exclaim, “Wow, that was fast. Who fixed something?”Locate performance hot spots using the profiler and software timersLearn to perform repeatable experiments to measure performance of code changesOptimize use of dynamically allocated variablesImprove performance of hot loops and functionsSpeed up string handling functionsRecognize efficient algorithms and optimization patternsLearn the strengths—and weaknesses—of C++ container classesView searching and sorting through an optimizer’s eyeMake efficient use of C++ streaming I/O functionsUse C++ thread-based concurrency features effectively
Optimized Cloud Based Scheduling (Studies In Computational Intelligence #759)
by John A. Leong Amandeep S. Sidhu Rong Kun TanThis book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics.
Optimized Computational Intelligence Driven Decision-Making: Theory, Application and Challenges (Industry 5.0 Transformation Applications)
by Minakhi Rout S. Balamurugan Hrudaya Kumar Tripathy Sushruta Mishra Samaresh MishraThis book covers a wide range of advanced techniques and approaches for designing and implementing computationally intelligent methods in different application domains which is of great use to not only researchers but also academicians and industry experts. Optimized Computational Intelligence (OCI) is a new, cutting-edge, and multidisciplinary research area that tackles the fundamental problems shared by modern informatics, biologically-inspired computation, software engineering, AI, cybernetics, cognitive science, medical science, systems science, philosophy, linguistics, economics, management science, and life sciences. OCI aims to apply modern computationally intelligent methods to generate optimum outcomes in various application domains. This book presents the latest technologies-driven material to explore optimized various computational intelligence domains. includes real-life case studies highlighting different advanced technologies in computational intelligence; provides a unique compendium of current and emerging hybrid intelligence paradigms for advanced informatics; reflects the diversity, complexity, and depth and breadth of this critical bio-inspired domain; offers a guided tour of computational intelligence algorithms, architecture design, and applications of learning in dealing with cognitive informatics challenges; presents a variety of intelligent and optimized techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional data analytics research in intelligent decision-making system dynamics; includes architectural models and applications-based augmented solutions for optimized computational intelligence. Audience The book will interest a range of engineers and researchers in information technology, computer science, and artificial intelligence working in the interdisciplinary field of computational intelligence.
Optimized Predictive Models in Health Care Using Machine Learning
by Sandeep Kumar Anuj Sharma Navneet Kaur Lokesh Pawar Rohit BajajOPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs. Other essential features of the book include: provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application; emphasizes validating and evaluating predictive models; provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; discusses the challenges and limitations of predictive modeling in healthcare; highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning.
Optimizing and Troubleshooting Hyper-V Networking
by Mitch Tulloch The Windows Server TeamThis scenario-focused title provides concise technical guidance and insights for troubleshooting and optimizing networking with Hyper-V. Written by experienced virtualization professionals, this little book packs a lot of value into a few pages, offering a lean read with lots of real-world insights and best practices for Hyper-V networking optimization in Windows Server 2012. Focused guide extends your knowledge and capabilities with Hyper-V networking in Windows Server 2012 Shares hands-on insights from a team of Microsoft virtualization experts Provides pragmatic troubleshooting and optimization guidance from the field
Optimizing and Troubleshooting Hyper-V Storage
by Mitch Tulloch The Windows Server TeamThis scenario-focused title provides concise technical guidance and insights for troubleshooting and optimizing storage with Hyper-V. Written by experienced virtualization professionals, this little book packs a lot of value into a few pages, offering a lean read with lots of real-world insights and best practices for Hyper-V storage optimization. Focused guide extends your knowledge and capabilities with Hyper-V storage in Windows Server 2012 Shares hands-on insights from a team of Microsoft virtualization experts Provides pragmatic troubleshooting and optimization guidance from the field
Optimizing Biofuel Production with Artificial Intelligence
by Arindam Kuila Depak KumarOptimizing Biofuel Production with Artificial Intelligence will help readers discover how integrating artificial intelligence with biotechnological advancements can revolutionize biofuel production, ensuring a sustainable energy future in response to pressing global challenges like pollution and climate change. This book presents artificial intelligence as a technique to aid the production of biofuels. Recently, tremendous developments have been made in energy and environmental biotechnologies, spurred by societal issues like pollution control, energy security, and climate change. Energy can be obtained from a variety of sources, including coal, oil, natural gas, solar, wind, and nuclear energy. The need to transition to new energy results from finite resources and economic sustainability. Biotechnological process optimization is crucial for ensuring a quality final product and boosting bioconversion performance efficiency. When combined with traditional simulation and modeling methods, artificial intelligence and computer technology can help define ideal process parameters and save total process costs. The energy sector can benefit from artificial intelligence in several ways, including increased asset efficiency, early detection and assessment of wildfire risks, assistance with vegetation management and storm recovery, and optimized energy use. The new frontier for energy is biomass.
Optimizing Citrix® XenDesktop® for High Performance
by Craig Thomas EllrodSuccessfully deploy XenDesktop sites for a high performance Virtual Desktop Infrastructure (VDI) About This Book * Size the VDI environment so the administrator has breathing room to design and build their XenDesktop systems efficiently * Use desktop virtualization tools to provide users fast, convenient access to their Windows Desktops * Understand the key pinch points in the resource layers such as; the Client layer, Network Layer, Access Layer, Control Layer, Services Layer and Resources Layer Who This Book Is For Citrix XenDesktop High Performance is written for administrators who would like to deploy Citrix XenDesktop in their enterprises with the aim of providing high efficiency. Basic familiarity with Citrix XenDesktop is assumed. What You Will Learn * Understand key concepts, terminology, and system requirements * Discover how components work in regards to virtualization and performance * Identify architectural resource layers and components * Explore the hypervisor virtualization software that runs on top of the hardware and learn how to tune it for maximum performance * Analyze client hardware and software, including thin clients and mobile devices In Detail Citrix XenDesktop is a suite of desktop virtualization tools designed to provide users with fast and convenient access to their Windows desktops and applications through any device. Virtual desktops mean that rather than setting up hundreds or thousands of individual computers in an enterprise, companies can instead opt to create servers with large amounts of memory, disk, and processing resources, and use virtualization to offer these resources to end users. The result of this is that users are provided with an experience that appears to be identical to having an individual desktop PC. Each user has some disk space, processor time, and memory allocated to them, as though it is present on their own physical machine, when in reality, the resources are physically present on a centralized server. This book starts by answering the basic questions you need to ask when considering XenDesktop, followed by methods of how you can properly size your server infrastructure for XenDesktop. You'll discover how to optimize the virtual machines used in XenDesktop, how to optimize your network for XenDesktop, and how to optimize the hypervisor and the cloud. You'll also learn how to monitor XenDesktop to maximize performance. By the end of the book, you will be able to plan, design, build, and deploy high performance XenDesktop Virtualization systems in enterprises. You will also know how to monitor and maintain your systems to ensure smooth operation. Style and approach This book is an all-inclusive guide that uncovers hidden and previously unpublished performance improvement areas for any XenDesktop site.
Optimizing Cloud Native Java: Practical Techniques for Improving JVM Application Performance
by Benjamin J. Evans James GoughPerformance tuning is an experimental science, but that doesn't mean engineers should resort to guesswork and folklore to get the job done. Yet that's often the case. With this practical book, intermediate to advanced Java technologists working with complex platforms will learn how to tune Java cloud applications for performance using a quantitative, verifiable, and repeatable approach.In response to the ubiquity of cloud computing, this updated edition of Optimizing Cloud Native Java addresses topics that are key to high performance of Java applications in the cloud. Many resources on performance tend to focus on the theory and internals of Java virtual machines, but this book discusses the low-level technical aspects within the context of performance-tuning practicalities and examines a wide range of aspects.With this book, you will:Learn how Java principles and technology make the best use of modern hardware, operating systems, and cloud stacksExamine the pitfalls of measuring Java performance numbers and the drawbacks of microbenchmarkingUnderstand how to package, deploy, operate, and debug Java/JVM applications in modern cloud environmentsApply emerging observability approaches to obtain deep understanding of cloud native applicationsUse Java language performance techniques including concurrent and distributed forms
Optimizing Databricks Workloads: Harness the power of Apache Spark in Azure and maximize the performance of modern big data workloads
by Anirudh Kala Anshul Bhatnagar Sarthak SarbahiAccelerate computations and make the most of your data effectively and efficiently on DatabricksKey FeaturesUnderstand Spark optimizations for big data workloads and maximizing performanceBuild efficient big data engineering pipelines with Databricks and Delta LakeEfficiently manage Spark clusters for big data processingBook DescriptionDatabricks is an industry-leading, cloud-based platform for data analytics, data science, and data engineering supporting thousands of organizations across the world in their data journey. It is a fast, easy, and collaborative Apache Spark-based big data analytics platform for data science and data engineering in the cloud.In Optimizing Databricks Workloads, you will get started with a brief introduction to Azure Databricks and quickly begin to understand the important optimization techniques. The book covers how to select the optimal Spark cluster configuration for running big data processing and workloads in Databricks, some very useful optimization techniques for Spark DataFrames, best practices for optimizing Delta Lake, and techniques to optimize Spark jobs through Spark core. It contains an opportunity to learn about some of the real-world scenarios where optimizing workloads in Databricks has helped organizations increase performance and save costs across various domains.By the end of this book, you will be prepared with the necessary toolkit to speed up your Spark jobs and process your data more efficiently.What you will learnGet to grips with Spark fundamentals and the Databricks platformProcess big data using the Spark DataFrame API with Delta LakeAnalyze data using graph processing in DatabricksUse MLflow to manage machine learning life cycles in DatabricksFind out how to choose the right cluster configuration for your workloadsExplore file compaction and clustering methods to tune Delta tablesDiscover advanced optimization techniques to speed up Spark jobsWho this book is forThis book is for data engineers, data scientists, and cloud architects who have working knowledge of Spark/Databricks and some basic understanding of data engineering principles. Readers will need to have a working knowledge of Python, and some experience of SQL in PySpark and Spark SQL is beneficial.
Optimizing Digital Competence through Microlearning: Flexible Approaches to Teacher Professional Development (SpringerBriefs in Education)
by Lucas KohnkeThis book examines teacher professional development (TPD) models to integrate microlearning into TPD training and serves as a concise but comprehensive introduction to the field. This book covers critical teacher professional development and microlearning aspects, including artificial intelligence, virtual reality, augmented reality, and mixed modalities. It is an important starting point for teachers, teacher-trainers, academics, and researchers interested in the principles and practices in teacher professional development through microlearning.
Optimizing Digital Strategy: How to Make Informed, Tactical Decisions that Deliver Growth
by Professor Christopher Bones James Hammersley Nick ShawOptimizing Digital Strategy explores the choices facing organizations in the rapidly changing world of technology-enabled business. From performance marketing through to personalization, on-demand retailing and AI, this book maps out commercial and customer-focused challenges and explains how leaders can get the most out of their digital strategies. Rather than rushing headlong into adopting the latest digital platforms, tools and technologies, the book challenges leaders to step back from the demands for constant investment in new technology and drive better returns from existing assets. Presenting a sustainable model of e-commerce that is appropriate to any individual organization's needs, Optimizing Digital Strategy addresses the repetitive dilemma between even more investment in technology and the need to improve margins and grow revenue. Illustrated by the authors' own digital work for global brands such as The Economist, Sky, O2, Regus, the Financial Times, Lidl and L.K.Bennett, this book shows how to balance the need to remain competitive, fully deliver customer expectations, and put resources behind investments that will deliver the best return.
Optimizing Engineering Problems through Heuristic Techniques (Science, Technology, and Management)
by J. Paulo Davim Kaushik Kumar Divya ZindaniThis book will cover heuristic optimization techniques and applications in engineering problems. The book will be divided into three sections that will provide coverage of the techniques, which can be employed by engineers, researchers, and manufacturing industries, to improve their productivity with the sole motive of socio-economic development. This will be the first book in the category of heuristic techniques with relevance to engineering problems and achieving optimal solutions. Features Explains the concept of optimization and the relevance of using heuristic techniques for optimal solutions in engineering problems Illustrates the various heuristics techniques Describes evolutionary heuristic techniques like genetic algorithm and particle swarm optimization Contains natural based techniques like ant colony optimization, bee algorithm, firefly optimization, and cuckoo search Offers sample problems and their optimization, using various heuristic techniques
Optimizing Generative AI Workloads for Sustainability: Balancing Performance and Environmental Impact in Generative AI
by Ishneet Kaur Dua Parth Girish PatelThis comprehensive guide provides practical strategies for optimizing Generative AI systems to be more sustainable and responsible. As advances in Generative AI such as large language models accelerate, optimizing these resource-intensive workloads for efficiency and alignment with human values grows increasingly urgent. The book starts with the concept of Generative AI and its wide-ranging applications, while also delving into the environmental impact of AI workloads and the growing importance of adopting sustainable AI practices. It then delves into the fundamentals of efficient AI workload management, providing insights into understanding AI workload characteristics, measuring performance, and identifying bottlenecks and inefficiencies. Hardware optimization strategies are explored in detail, covering the selection of energy-efficient hardware, leveraging specialized AI accelerators, and optimizing hardware utilization and scheduling for sustainable operations. You are also guided through software optimization techniques tailored for Generative AI, including efficient model architecture, compression, and quantization methods, and optimization of software libraries and frameworks. Data management and preprocessing strategies are also addressed, emphasizing efficient data storage, cleaning, preprocessing, and augmentation techniques to enhance sustainability throughout the data life cycle. The book further explores model training and inference optimization, cloud and edge computing strategies for Generative AI, energy-efficient deployment and scaling techniques, and sustainable AI life cycle management practices, and concludes with real-world case studies and best practices By the end of this book, you will take away a toolkit of impactful steps you can implement to minimize the environmental harms and ethical risks of Generative AI. For organizations deploying any type of generative model at scale, this essential guide provides a blueprint for developing responsible AI systems that benefit society. What You Will Learn Understand how Generative AI can be more energy-efficient through improvements such as model compression, efficient architecture, hardware optimization, and carbon footprint tracking Know the techniques to minimize data usage, including evaluation, filtering, synthesis, few-shot learning, and monitoring data demands over time Understand spanning efficiency, data minimization, and alignment for comprehensive responsibility Know the methods for detecting, understanding, and mitigating algorithmic biases, ensuring diversity in data collection, and monitoring model fairness Who This book Is For Professionals seeking to adopt responsible and sustainable practices in their Generative AI work; leaders and practitioners who need actionable strategies and recommendations that can be implemented directly in real-world systems and organizational workflows; ML engineers and data scientists building and deploying Generative AI systems in industry settings; and researchers developing new generative AI techniques, such as at technology companies or universities
Optimizing Hadoop for MapReduce
by Khaled TannirThis book is an example-based tutorial that deals with Optimizing Hadoop for MapReduce job performance. If you are a Hadoop administrator, developer, MapReduce user, or beginner, this book is the best choice available if you wish to optimize your clusters and applications. Having prior knowledge of creating MapReduce applications is not necessary, but will help you better understand the concepts and snippets of MapReduce class template code.
Optimizing HPC Applications with Intel® Cluster Tools: Hunting Petaflops
by Alexander Supalov Andrey Semin Michael Klemm Christopher DahnkenOptimizing HPC Applications with Intel® Cluster Tools takes the reader on a tour of the fast-growing area of high performance computing and the optimization of hybrid programs. These programs typically combine distributed memory and shared memory programming models and use the Message Passing Interface (MPI) and OpenMP for multi-threading to achieve the ultimate goal of high performance at low power consumption on enterprise-class workstations and compute clusters. The book focuses on optimization for clusters consisting of the Intel® Xeon processor, but the optimization methodologies also apply to the Intel® Xeon Phi(tm) coprocessor and heterogeneous clusters mixing both architectures. Besides the tutorial and reference content, the authors address and refute many myths and misconceptions surrounding the topic. The text is augmented and enriched by descriptions of real-life situations. What you'll learn Practical, hands-on examples show how to make clusters and workstations based on Intel® Xeon processors and Intel® Xeon Phi(tm) coprocessors "sing" in Linux environments How to master the synergy of Intel® Parallel Studio XE 2015 Cluster Edition, which includes Intel® Composer XE, Intel® MPI Library, Intel® Trace Analyzer and Collector, Intel® VTune(tm) Amplifier XE, and many other useful tools How to achieve immediate and tangible optimization results while refining your understanding of software design principles Who this book is for Software professionals will use this book to design, develop, and optimize their parallel programs on Intel platforms. Students of computer science and engineering will value the book as a comprehensive reader, suitable to many optimization courses offered around the world. The novice reader will enjoy a thorough grounding in the exciting world of parallel computing. Table of Contents Foreword by Bronis de Supinski, CTO, Livermore Computing, LLNL Introduction Chapter 1: No Time to Read this Book? Chapter 2: Overview of Platform Architectures Chapter 3: Top-Down Software Optimization Chapter 4: Addressing System Bottlenecks Chapter 5: Addressing Application Bottlenecks: Distributed Memory Chapter 6: Addressing Application Bottlenecks: Shared Memory Chapter 7: Addressing Application Bottlenecks: Microarchitecture Chapter 8: Application Design Considerations