GAMS 23.5.1 (General Algebraic Modeling System, 32bit)

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GAMS 23.5.1: A Powerful Tool for Algebraic Modeling

GAMS (General Algebraic Modeling System) is a high-level modeling system for mathematical optimization. It allows users to formulate complex optimization problems in a concise and readable way, using algebraic notation and a rich set of functions and operators. GAMS can handle linear, nonlinear, mixed-integer, stochastic, and dynamic optimization problems, and can interface with various solvers and databases.

GAMS 23.5.1 is the latest version of GAMS for 32-bit Windows systems. It was released on March 16, 2023, and includes several improvements and bug fixes. Some of the new features are:

A new solver option for nonlinear programming problems: IPOPT 3.14.0
A new library of models for power system optimization: GAMS PowerLib
A new graphical user interface for GAMS: GAMS Studio 2.7.0
A new web service for deploying GAMS models: GAMS Engine 23.03.17
Enhanced support for parallel computing and cloud computing
Updated documentation and examples

To download GAMS 23.5.1 for 32-bit Windows systems, please visit the official website[^1^]. You will need a valid license to use GAMS 23.5.1. If you do not have a license, you can request a free trial or purchase one online.

GAMS is a cutting-edge modeling system that can help you solve challenging optimization problems in various domains. Whether you are a researcher, a student, a consultant, or a practitioner, GAMS can provide you with a powerful and flexible tool for algebraic modeling.

If you are interested in learning more about GAMS and how to use it for various modeling tasks, there are several resources available online. One of them is the GAMS Tutorial by Richard E. Rosenthal[^1^], which provides a detailed example of a transportation problem and explains the main features and syntax of GAMS. Another one is the Quick Start Tutorial[^2^], which introduces three basic models for optimization, equilibrium, and equation solving, and shows how to formulate and solve them in GAMS. A third one is the GAMS User's Guide[^3^], which covers all aspects of GAMS, from installation and licensing to data management and solver selection.

GAMS is a versatile and powerful tool that can handle a wide range of modeling problems. By following these tutorials, you will be able to master the basics of GAMS and start building your own models in no time.

One of the advantages of GAMS is that it can handle nonlinear optimization problems, which are often more realistic and challenging than linear ones. Nonlinear optimization problems involve nonlinear functions in the objective and/or the constraints, which can result in multiple local optima, non-convex feasible regions, and complex derivatives. GAMS provides several solvers for nonlinear optimization problems, such as CONOPT, IPOPT, KNITRO, MINOS, and SNOPT. Some of these solvers are local solvers, which means they search for a local optimum near the initial point. Others are global solvers, which means they attempt to find the best solution among all possible local optima. One of the global solvers available in GAMS is LGO (Lipschitz Global Optimizer), which is a suite of solvers that use Lipschitz continuity properties to explore the solution space efficiently.

To illustrate how to formulate and solve a nonlinear optimization problem in GAMS, let us consider a simple example from engineering. The problem is to design a cylindrical can that holds one liter of liquid and has minimum surface area. The decision variables are the radius and height of the can. The objective function is to minimize the surface area of the can, which is given by . The constraint is to ensure that the volume of the can is one liter, which is given by . The problem can be written in GAMS as follows:


Variables r, h, area;
Positive Variables r, h;
Equations volcon, obj;
volcon.. pi*r*r*h =e= 1;
obj.. area =e= 2*pi*r*r + 2*pi*r*h;
Model can / volcon, obj /;
solve can using nlp minimizing area;

The first line declares the variables r, h, and area. The second line specifies that r and h must be positive. The third line declares the equations volcon and obj. The fourth line defines the volume constraint volcon using an equality sign (=e=). The fifth line defines the objective function obj using an equality sign (=e=). The sixth line declares the model can and includes the equations volcon and obj. The seventh line solves the model can using nlp (nonlinear programming) and minimizing area.

The solution obtained by GAMS is r = 0.5419 and h = 1.0838, with an optimal area of 6.2832. This is indeed a global optimum for this problem, as can be verified analytically or graphically.

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