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A GAMS Tutorial by Richard E. Rosenthal
1 Introduction
The introductory part of this book ends with a detailed example of the use of GAMS for formulating, solving, and analyzing
a small and simple optimization problem. Richard E. Rosenthal of the Naval Postgraduate School in Monterey, California
wrote it. The example is a quick but complete overview of GAMS and its features. Many references are made to other parts
of the book, but they are only to tell you where to look for more details; the material here can be read profitably without
reference to the rest of the book.
The example is an instance of the transportation problem of linear programming, which has historically served as a 'laboratory
animal' in the development of optimization technology. [See, for example, Dantzig (1963) 1. ] It is a good choice for
illustrating the power of algebraic modeling languages like GAMS because the transportation problem, no matter how large
the instance at hand, possesses a simple, exploitable algebraic structure. You will see that almost all of the statements in the
GAMS input file we are about to present would remain unchanged if a much larger transportation problem were considered.
In the familiar transportation problem, we are given the supplies at several plants and the demands at several markets for a
single commodity, and we are given the unit costs of shipping the commodity from plants to markets. The economic question
is: how much shipment should there be between each plant and each market so as to minimize total transport cost?
The algebraic representation of this problem is usually presented in a format similar to the following
Equations
The power of algebraic modeling languages like GAMS is most apparent in the creation of the equations and inequalities that
comprise the model under construction. This is because whenever a group of equations or inequalities has the same algebraic
structure, all the members of the group are created simultaneously, not individually.
提供**过25个广泛和多样化的求解器组合,包括所有预期的商业化求解器。
● LP/MIP/QCP/MIQCP: CPLEX, GUROBI, MOSEK, XPRESS
● NLP: CONOPT, IPOPTH, KNITRO, MINOS, SNOPT
● MINLP: ALPHAECP, ANTIGONE, BARON, DICOPT, OQNLP, SBB
● 混合互补问题求解器(MCP)、平衡约束数学规划求解器(MPEC)和约束非线性系统求解器(CNS)
● 免费到每个GAMS系统中的 (比如 BONMIN (MINLP), CBC (LP, MIP), COUENNE (MINLP), IPOPT (NLP)。教
育版还包括了SCIP和SOPLEX。
GAMS允许用户在某种程度上,用跟数学描述非常相似的方式来制定数学模型。看一下这些例子就能说明GAMS模型
的基本结构和特征以及与数学表达式的关系。GAMS让用户专注建模,通过要求简洁和的实体和关系规范,鼓励良好
的建模习惯。GAMS语言与通用编程语言形式相似,因此对于有编程经验的人来说是熟悉的。由于模型的制定方式在某种
程度上与它的数学描述类似,所以不仅是程序员,实际领域的也能理解和维护。GAMS专注于建模并且允许做所有相
关的事。
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