Hurlbert, Glenn H  | Springer Verlag

100,480원  | 20110316  | 9780387791470

This undergraduate textbook is written for a junior/senior level course on linear optimization. Unlike other texts, the treatment follows the "modified Moore method" approach in which examples and proof opportunities are worked into the text in order to encourage students to develop some of the content through their own experiments and arguments while they are reading the text.

 | Taylor & Francis

173,220원  | 20210101  | 9781439887462

The latest edition of this engaging, highly interactive world atlas delivers National Geographic's cartographic expertise at a mid-price and mid-size format. Thoroughly updated and newly designed, it will be a go-to reference.

 | Athena Scientific

160,000원  | 19970201  | 9781886529199

This book provides a unified, insightful, and modern treatment of linear optimization, that is, linear programming, network flow problems, and discrete optimization. It includes classical topics as well as the state of the art, in both theory and practice.

Rader, David J.  | Wiley

250,380원  | 20100826  | 9780470484517

Uniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering.

Padberg, M.  | Springer

204,850원  | 19990701  | 9783540658337

 | LAP Lambert Academic Publishing

152,720원  | 20180524  | 9786137380079

The book was motivated by Prof Mashelkar's slogan of "innovations for more", which has hurled India's talent for science and innovation to the international arena. Such innovations have become the cornerstone of modern development that strives for "ultra-low-cost designs" to cater for increased global population, amidst abject poverty. Some innovations have direct consequences on wellbeing of mankind and others reach them indirectly, yet they all are intended to make life better and easier.

 | Springer Nature B.V.

74,480원  | 20220819  | 9783031072154

This book provides a comprehensive discussion and new insights about linear optimization of content metrics to improve the automatic Evaluation of Text Summaries (ETS). The reader is first introduced to the background and fundamentals of the ETS. Afterward, state-of-the-art evaluation methods that require or do not require human references are described.

Fiedler, Miroslav/ Ramik, Jaroslav/ Rohn, Jiri/ Zi  | Springer

0원  | 20060301  | 9780387326979

Linear programming attracted the interest of mathematicians during and after World War II when the first computers were constructed and methods for solving large linear programming problems were sought in connection with specific practical problems or example, providing logistical support for the U.S. Armed Forces or modeling national economies. Early attempts to apply linear programming methods to solve practical problems failed to satisfy expectations. There were various reasons for the failure. One of them, which is the central topic of this book, was the inexactness of the data used to create the models. This phenomenon, inherent in most pratical problems, has been dealt with in several ways. At first, linear programming models used "average" values of inherently vague coefficients, but the optimal solutions of these models were not always optimal for the original problem itself. Later researchers developed the stochastic linear programming approach, but this too has its limitations. Recently, interest has been given to linear programming problems with data given as intervals, convex sets and/or fuzzy sets. The individual results of these studies have been promising, but the literature has not presented a unified theory. Linear Optimization Problems with Inexact Data attempts to present a comprehensive treatment of linear optimization with inexact data, summarizing existing results and presenting new ones within a unifying framework.

 | Content Technologies, Inc.

0원  | 20101216  | 9781617446924

Never HIGHLIGHT a Book Again! Includes all testable terms, concepts, persons, places, and events. Cram101 Just the FACTS101 studyguides gives all of the outlines, highlights, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram101 is Textbook Specific. Accompanies: 9780470484517. This item is printed on demand.

Gerard Sierksma, PhD, Yori Zwols, PhD  | Taylor & Francis

168,190원  | 20150508  | 9781498710169

Presenting a strong and clear relationship between theory and practice, Linear and Integer Optimization: Theory and Practice is divided into two main parts. The first covers the theory of linear and integer optimization, including both basic and advanced topics. Dantzig’s simplex algorithm, duality, sensitivity analysis, integer optimization models, and network models are introduced. More advanced topics also are presented including interior point algorithms, the branch-and-bound algorithm, cutting planes, complexity, standard combinatorial optimization models, the assignment problem, minimum cost flow, and the maximum flow/minimum cut theorem. The second part applies theory through real-world case studies. The authors discuss advanced techniques such as column generation, multiobjective optimization, dynamic optimization, machine learning (support vector machines), combinatorial optimization, approximation algorithms, and game theory. Besides the fresh new layout and completely redesigned figures, this new edition incorporates modern examples and applications of linear optimization. The book now includes computer code in the form of models in the GNU Mathematical Programming Language (GMPL). The models and corresponding data files are available for download and can be readily solved using the provided online solver. This new edition also contains appendices covering mathematical proofs, linear algebra, graph theory, convexity, and nonlinear optimization. All chapters contain extensive examples and exercises. This textbook is ideal for courses for advanced undergraduate and graduate students in various fields including mathematics, computer science, industrial engineering, operations research, and management science.

 | Springer Nature B.V.

74,480원  | 20170613  | 9781493970544

?This textbook on Linear and Nonlinear Optimization is intended for graduate and advanced undergraduate students in operations research and related fields. It is both literate and mathematically strong, yet requires no prior course in optimization.

 | Mercury Learning & Information

137,370원  | 20190402  | 9781683923473

Designed for engineers, mathematicians, computer scientists, financial analysts, and anyone interested in using numerical linear algebra, matrix theory, and game theory concepts to maximize efficiency in solving applied problems. The book emphasizes the solution of various types of linear programming problems by using different types of software.

Griva, Igor  | Society for Industrial & Appli

194,440원  | 20120430  | 9780898716610

 | Springer

150,840원  | 20250112  | 9783031434884

Coverage includes everything you need to know to analyze and optimize a vehicle’s vibration, including vehicle vibration components, vehicle vibration analysis, flat ride vibration, tire-road separations, and smart suspensions.

Charu C. Aggarwal  | Springer

110,000원  | 20200603  | 9783030403430

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this text book together with access to a solution’s manual. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.