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The Design of Approximation Algorithms
Discrete optimization problems are everywhere, from traditional operations research planning problems, such as scheduling, facility location, and network design; to computer science problems in databases; to advertising issues in viral marketing. Yet most such problems are NP-hard. Thus unless P = NP, there are no efficient algorithms to find optimal solutions to such problems. This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The book is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programm...
Network Flow Algorithms
Network flow theory has been used across a number of disciplines, including theoretical computer science, operations research, and discrete math, to model not only problems in the transportation of goods and information, but also a wide range of applications from image segmentation problems in computer vision to deciding when a baseball team has been eliminated from contention. This graduate text and reference presents a succinct, unified view of a wide variety of efficient combinatorial algorithms for network flow problems, including many results not found in other books. It covers maximum flows, minimum-cost flows, generalized flows, multicommodity flows, and global minimum cuts and also presents recent work on computing electrical flows along with recent applications of these flows to classical problems in network flow theory.
Network Flow Algorithms
Offers an up-to-date, unified treatment of combinatorial algorithms to solve network flow problems for graduate students and professionals.
The Design of Approximation Algorithms
Discrete optimization problems are everywhere, from traditional operations research planning (scheduling, facility location and network design); to computer science databases; to advertising issues in viral marketing. Yet most such problems are NP-hard; unless P = NP, there are no efficient algorithms to find optimal solutions. This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The book is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization. Each chapter in the first section is devoted to a single algorithmic technique applied to several different problems, with more sophisticated treatment in the second section. The book also covers methods for proving that optimization problems are hard to approximate. Designed as a textbook for graduate-level algorithm courses, it will also serve as a reference for researchers interested in the heuristic solution of discrete optimization problems.
Strategic Management and Business Analysis
Strategic Business Analysis shows students how to carry out a strategic analysis of a business, with clear guidelines on where and how to apply the core strategic techniques and models that are the integral tools of strategic management. The authors identify the key questions in strategic analysis and provide an understandable framework for answering these questions. Several case studies are used to focus understanding and enable a more thorough analysis of the concepts and issues, especially useful for students involved with case study analysis. Accompanying the text is a CD-Rom containing the models, tutorial guidance, and a PowerPoint presentation. A blank template is provided for each model, enabling students to actively interact and enter their own data - an effective 'what if...' facility. This will enable students to appreciate the limitations as well as the advantages of the strategic models.
Quantitative Ethnography
How can we make sense of make sense of the deluge of information in the digital age? The new science of Quantitative Ethnography dissolves the boundaries between quantitative and qualitative research to give researchers tools for studying the human side of big data: to understand not just what data says, but what it tells us about the people who created it. Thoughtful, literate, and humane, Quantitative Ethnography integrates data-mining, discourse analysis, psychology, statistics, and ethnography into a brand-new science for understanding what people do and why they do it. Packed with anecdotes, stories, and clear explanations of complex ideas, Quantitative Ethnography is an engaging introduction to research methods for students, an introduction to data science for qualitative researchers, and an introduction to the humanities for statisticians--but also a compelling philosophical and intellectual journey for anyone who wants to understand learning, culture and behavior in the age of big data.
Germany since 1789
This essential text provides a clear and engaging introduction to the history of modern Germany. The updated and expanded new edition now takes the story back to 1789 and brings it right up to the present day, adopting a controversy-led approach throughout. Visual evidence, maps, documents and key event boxes support the text and aid learning.
Geometric Approximation Algorithms
Exact algorithms for dealing with geometric objects are complicated, hard to implement in practice, and slow. Over the last 20 years a theory of geometric approximation algorithms has emerged. These algorithms tend to be simple, fast, and more robust than their exact counterparts. This book is the first to cover geometric approximation algorithms in detail. In addition, more traditional computational geometry techniques that are widely used in developing such algorithms, like sampling, linear programming, etc., are also surveyed. Other topics covered include approximate nearest-neighbor search, shape approximation, coresets, dimension reduction, and embeddings. The topics covered are relatively independent and are supplemented by exercises. Close to 200 color figures are included in the text to illustrate proofs and ideas.
Making a Place for Community
When pundits refer to the death of community, they are speaking of a number of social ills, which include, but are not limited to, the general increase in isolation and cynicism of our citizens, widespread concerns about declining political participation and membership in civic organizations, and periodic outbursts of small town violence. Making a Place for Community argues that this death of community is being caused by contemporary policies that, if not changed, will continue to foster the decline of community. Increased capital flow between nations is not at the root of the problem, however, increased capital flow within our nation is. Small towns shouldn't have to hope for a prison to open nearby and downtown centers shouldn't sit empty as suburban sparwl encroaches, but they do and it's a result of widely agreed upon public policies.
