AMPL : a modeling language for mathematical programming
著者
書誌事項
AMPL : a modeling language for mathematical programming
Brooks/Cole-Thomson Learning, c2003
2nd ed
大学図書館所蔵 全11件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references and index
内容説明・目次
内容説明
AMPL is a language for large-scale optimization and mathematical programming problems in production, distribution, blending, scheduling, and many other applications. Combining familiar algebraic notation and a powerful interactive command environment, AMPL makes it easy to create models, use a wide variety of solvers, and examine solutions. Though flexible and convenient for rapid prototyping and development of models, AMPL also offers the speed and generality needed for repeated large-scale production runs. This book, written by the creators of AMPL, is a complete guide for modelers at all levels of experience. It begins with a tutorial on widely used linear programming models, and presents all of AMPL's features for linear programming with extensive examples. Additional chapters cover network, nonlinear, piecewise-linear, and integer programming; database and spreadsheet interactions; and command scripts. Most chapters include exercises. Download free versions of AMPL and several solvers from www.ampl.com for experimentation, evaluation, and education. The Web site also lists vendors of the commercial version of AMPL and numerous solvers.
目次
1. PRODUCTION MODELS: MAXIMIZING PROFITS. A two-variable linear program. The two-variable linear program in AMPL. A linear programming model. The linear programming model in AMPL. Adding lower bounds to the model. Adding resource constraints to the model. AMPL interfaces. 2. DIET AND OTHER INPUT MODELS: MINIMIZING COSTS. A linear program for the diet problem. An AMPL model for the diet problem. Using the AMPL diet model. Generalizations to blending, economics, and scheduling. 3. TRANSPORTATION AND ASSIGNMENT MODELS. A linear program for the transportation problem. An AMPL model for the transportation problem. Other interpretations of the transportation model. 4. BUILDING LARGER MODELS. A multicommodity transportation model. A multiperiod production model. A model of production and transportation. 5. SIMPLE SETS AND INDEXING. Unordered sets. Sets of numbers. Set operations. Set membership operations and functions. Indexing expressions. Ordered sets. 6. COMPOUND SETS AND INDEXING. Sets of ordered pairs. Subsets and slices of ordered pairs. Sets of longer tuples. Operations on sets of tuples. Indexed collections of sets. 7. PARAMETERS AND EXPRESSIONS. Parameter declarations. Arithmetic expressions. Logical and conditional expressions. Restrictions on parameters. Computed parameters. Randomly generated parameters. Logical parameters. Symbolic parameters. 8. LINEAR PROGRAMS: VARIABLES, OBJECTIVES AND CONSTRAINTS. Variables. Linear expressions. Objectives. Constraints. 9. SPECIFYING DATA. Formatted data: the data command. Data in lists. Data in tables. Other features of data statements. Reading unformatted data: the read command. 10. DATABASE ACCESS. General principles of data correspondence. Examples of table-handling statements. Reading data from relational tables. Writing data to relational tables. Reading and writing the same table. Indexed collections of tables and columns. Standard and built-in table handlers. 11. MODELING COMMANDS. General principles of commands and options. Setting up and solving models and data. Modifying data. Modifying models. 12. DISPLAY COMMANDS. Browsing through results: the display command. Formatting options for display. Numeric options for display. Other output commands: print and printf. Related solution values. Other display features for models and instances. General facilities for manipulating output. 13. COMMAND SCRIPTS. Running scripts: include and commands. Iterating over a set: the for statement. Iterating subject to a condition: the repeat statement. Testing a condition: the if-then-else statement. Terminating a loop: break and continue. Stepping through a script. Manipulating character strings. 14. INTERACTIONS WITH SOLVERS. Presolve. Retrieving results from solvers. Exchanging information with solvers via suffixes. Alternating between models. Named problems. 15. NETWORK LINEAR PROGRAMS. Minimum-cost transshipment models. Other network models. Declaring network models by node and arc. Rules for node and arc declarations. Solving network linear programs. 16. COLUMNWISE FORMULATIONS. An input-output model. A scheduling model. Rules for columnwise formulations. 17. PIECEWISE-LINEAR PROGRAMS. Cost terms. Common two-piece and three-piece terms. Other piecewise-linear functions. Guidelines for piecewise-linear optimization. 18. NONLINEAR PROGRAMS. Sources of nonlinearity. Nonlinear variables. Nonlinear expressions. Pitfalls of nonlinear programming. 19. COMPLEMENTARITY PROBLEMS. Sources of complementarity. Forms of complementarity constraints. Working with complementarity constraints. 20. INTEGER LINEAR PROGRAMS. Integer variables. Zero-one variables and logical conditions. Practical considerations in integer programming. APPENDIX A: AMPL REFERENCE MANUAL. INDEX.
「Nielsen BookData」 より