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Combinatorial (or discrete) optimization is one of the most active fields in the interface of operations research, computer science, and applied math- ematics. Combinatorial optimization problems arise in various applications, including communications network design, VLSI design, machine vision, air- line crew scheduling, corporate planning, computer-aided design and man- ufacturing, database query design, cellular telephone frequency assignment, constraint directed reasoning, and computational biology. Furthermore, combinatorial optimization problems occur in many diverse areas such as linear and integer programming, graph theory, artificial intelligence, and number theory. All these problems, when formulated mathematically as the minimization or maximization of a certain function defined on some domain, have a commonality of discreteness. Historically, combinatorial optimization starts with linear programming. Linear programming has an entire range of important applications including production planning and distribution, personnel assignment, finance, alloca- tion of economic resources, circuit simulation, and control systems. Leonid Kantorovich and Tjalling Koopmans received the Nobel Prize (1975) for their work on the optimal allocation of resources. Two important discover- ies, the ellipsoid method (1979) and interior point approaches (1984) both provide polynomial time algorithms for linear programming. These algo- rithms have had a profound effect in combinatorial optimization. Many polynomial-time solvable combinatorial optimization problems are special cases of linear programming (e.g. matching and maximum flow). In addi- tion, linear programming relaxations are often the basis for many approxi- mation algorithms for solving NP-hard problems (e.g. dual heuristics).
Presenting the science of statistical process control (SPC) and the related concepts of total quality management (TQM) and design of experiments (DOE), this remarkable reference demonstrates ways to track industrial processes and performance -- integrating related areas such as engineering process control, statistical reasoning in TQM, robust parameter design, control charts, multivariate process monitoring, capability indices, experimental design, empirical model building, and process optimization. Written by more than 25 renowned researchers with broad experience with on-the-job problem solving, Statistical Process Monitoring and Optimization showcases concepts such as "people-based management" and continuous improvement...examines leadership styles in European, American, and Asian companies...describes means of quantifying factors such as employee loyalty and customer satisfaction and their relation to profitability...explores multivariate diagnosis theory in application to multioperational and multi-index systems...assesses uses of Markov chains in relation to ARL and associated sampling distributions...explains statistical models for dynamic systems, change-point analysis, generalized linear models, and key algorithms...elucidates replication strategies and process capability...reviews the interplay of intraorganizational domains such as finance, marketing, resource allocation, software engineering, and strategic planning...and more. Containing over 1000 references, drawings, tables, and equations, Statistical Process Monitoring and Optimization is a must-read resource for applied statisticians; industrial, quality control, and management science engineers; and upper-level undergraduate and graduate students in these disciplines. Book jacket.
It is difficult finding someone who really knows what they are doing with Internet Marketing and Search Engine Optimization. There are many that would like you to believe they are experts, but they could very well get your site de-listed. This is a short guide designed to aid high end professionals to learn enough about Internet Marketing and Search Engine Optimization in order to intelligently hire the right person for their marketing efforts online.
This book offers the best approach toward communicating the intricacies of marketing research and its usefulness to the marketing organization. This highly regarded text focuses on market intelligence, strategy, theory, and application and retains its coverage of the most advanced and current marketing research methodologies. Pointing out these methodologies' limitations and strengths, the book also brings to the forefront the relevance of marketing intelligence, the power of the Internet in marketing research applications, and much more. Suitable for students in the intermediate or advanced courses.
The vast majority of important applications in science, engineering and applied science are characterized by the existence of multiple minima and maxima, as well as first, second and higher order saddle points. The area of Deterministic Global Optimization introduces theoretical, algorithmic and computational adÂ vances that (i) address the computation and characterization of global minima and maxima, (ii) determine valid lower and upper bounds on the global minima and maxima, and (iii) address the enclosure of all solutions of nonlinear conÂ strained systems of equations. Global optimization applications are widespread in all disciplines and they range from atomistic or molecular level to process and product level representations. The primary goal of this book is three fold : first, to introduce the reader to the basics of deterministic global optimization; second, to present important theoretical and algorithmic advances for several classes of mathematical probÂ lems that include biconvex and bilinear; problems, signomial problems, general twice differentiable nonlinear problems, mixed integer nonlinear problems, and the enclosure of all solutions of nonlinear constrained systems of equations; and third, to tie the theory and methods together with a variety of important applications.