Cool PC Parts
A practical, accessible guide to optimization problems with discrete or integer variables<br> <br> Integer Programming stands out from other textbooks by explaining in clear and simple terms how to construct custom-made algorithms or use existing commercial software to obtain optimal or near-optimal solutions for a variety of real-world problems, such as airline timetables, production line schedules, or electricity production on a regional or national scale.<br> <br> Incorporating recent developments that have made it possible to solve difficult optimization problems with greater accuracy, author Laurence A. Wolsey presents a number of state-of-the-art topics not covered in any other textbook. These include improved modeling, cutting plane theory and algorithms, heuristic methods, and branch-and-cut and integer programming decomposition algorithms. This self-contained text:<br> * Distinguishes between good and bad formulations in integer programming problems<br> * Applies lessons learned from easy integer programs to more difficult problems<br> * Demonstrates with applications theoretical and practical aspects of problem solving<br> * Includes useful notes and end-of-chapter exercises<br> * Offers tremendous flexibility for tailoring material to different needs<br> <br> Integer Programming is an ideal text for courses in integer/mathematical programming-whether in operations research, mathematics, engineering, or computer science departments. It is also a valuable reference for industrial users of integer programming and researchers who would like to keep up with advances in the field.
Dynamic programming has long been applied to numerous areas in mat- matics, science, engineering, business, medicine, information systems, b- mathematics, arti?cial intelligence, among others. Applications of dynamic programming have increased as recent advances have been made in areas such as neural networks, data mining, soft computing, and other areas of com- tational intelligence. The value of dynamic programming formulations and means to obtain their computational solutions has never been greater. This book describes the use of dynamic programming as a computational tool to solve discrete optimization problems. (1) We ?rst formulate large classes of discrete optimization problems in dynamic programming terms, speci?cally by deriving the dynamic progr- ming functional equations (DPFEs) that solve these problems. A text-based language, gDPS, for expressing these DPFEs is introduced. gDPS may be regarded as a high-level speci?cation language, not a conventional procedural computer programming language, but which can be used to obtain numerical solutions. (2)Wethende?neandexaminepropertiesofBellmannets, aclassofPetri nets that serves both as a formal theoretical model of dynamic programming problems, and as an internal computer data structure representation of the DPFEs that solve these problems. (3)Wealsodescribethedesign, implementation, anduseofasoftwaretool, calledDP2PN2Solver, for solving DPFEs. DP2PN2Solver may be regarded as a program generator, whose input is a DPFE, expressed in the input spec- cation language gDPS and internally represented as a Bellman net, and whose output is its numerical solution that is produced indirectly by the generation of solver code, which when executed yields the desired solution."
Automatic Quantum Computer Programming provides an introduction to quantum computing for non-physicists, as well as an introduction to genetic programming for non-computer-scientists. The book explores several ways in which genetic programming can support automatic quantum computer programming and presents detailed descriptions of specific techniques, along with several examples of their human-competitive performance on specific problems. Source code for the author's QGAME quantum computer simulator is included as an appendix, and pointers to additional online resources furnish the reader with an array of tools for automatic quantum computer programming.
This text is written for the business major with enough mathematical background to appreciate an occasional departure from a main emphasis on applications.
The first five chapters discuss linear problems with linear programming the central topic. The necessary matrix algebra background is developed in Chapter 2. Chapters 6 and 7 require differential calculus at a level comparable to that of first year engineering and science students. The key elements of calculus needed for optimization are recalled at the beginning of Chapter 6. The eighth chapter is devoted to integer programming including branch and bound algorithms for the knapsack and traveling salesman problems and an emphasis on problem formulation. Chapter 9 is a short introduction to dynamic programming and the last chapter contains case studies at a level a little higher than the problems in the text.
Cool PC Parts Articles
Cool PC Parts Books
Cool PC Parts