Ultimate course to GAMS
Common problems in GAMS
Solvers and models in GAMS
- Being familiar to basic Algebra is recommended
- No previous knowledge on optimization or GAMS is required
Mathematical optimization is an extremely powerful analytical tool which uses mathematical models to choose the best element from a set of alternatives. Many of world’s biggest companies in various industries use mathematical optimization to choose the best course of action for their business and solve extremely complex problems.
Mathematical optimization is used for solving problems in many disciplines including Physics, Biology, Engineering, Economics, and Business management.
The General Algebraic Modeling System (GAMS) is a high-level modeling system for mathematical programming and optimization. It consists of a language compiler and a stable of integrated high-performance solvers. GAMS is tailored for complex, large scale modeling applications, and allows you to build large maintainable models that can be adapted quickly to new situations. GAMS is specifically designed for modeling linear, nonlinear and mixed integer optimization problems.
In this course we are going to get you started with GAMS to make you familiar with the software’s environment and the general flow of modeling and solving problems in GAMS. Then in the next section, we are going to have a crash course on GAMS and how to properly use it to model and solve problems and also how to understand and modify your solution and choose the proper model type and solver for your model. After that in the third section we are going to model and solve some common problems in GAMS starting from simpler problems all the way up to more complex multi-objective problems.
Essentially this course consists of :
- Basics of optimization and mathematical modeling
- A crash course on commands in GAMS and the syntax of the it’s code
- How to control the flow of the program
- Functions and data
- Reading and understanding solution reports
- Detailed explanation on every step from modeling to coding
- People interested in mathematical modeling
- Engineering students
- Applied math students
- Those interested in optimization
- Managers persuing better decision making