Udemy – Complete guide to Modern Mathematical Optimization in GAMS 2020-10
Udemy – Complete guide to Modern Mathematical Optimization in GAMS 2020-10 Downloadly IRSpace
Complete guide to Modern Mathematical Optimization in GAMS is a course on mathematical optimization with Gomez software (GAMS) published by Udemy Academy. To participate in this training course, you do not need any background knowledge in the field of programming and mathematical optimization, and all people with any level of knowledge can participate in this course. In this course, you will not work with specific and specialized issues and you will be satisfied with only a set of general issues and challenges. The target audience of this course is not limited to a specific industry or discipline and everyone can benefit from this course in some way, but engineers, programmers, students of applied mathematics and operations researchers can benefit the most from this course.
What you will learn in Complete guide to Modern Mathematical Optimization in GAMS
- Initial installation of Gomez (GAMS) on different operating systems
- Familiarity with the concept and principles of mathematical optimization theory
- Objective function
- Constraint
- Meaning and causes of various errors and mistakes in the optimization process and ways to fix them
- Read basic information from an Excel file
- Save final information and data in an Excel file
- Multi-Objective optimization in Gomez software
- Conditional programming and conditional expressions in Gomez
- Loops in Gomz
- Optimization of mathematical models
Course specifications
Publisher: Udemy
Instructors: Optimization Team
Language: English
Level: Introductory to Advanced
Number of Lessons: 23
Duration: 2 hours and 56 minutes
Course topics

Complete guide to Modern Mathematical Optimization in GAMS Prerequisites
You can start now without any prior knowledge in GAMS
Pictures

Introduction Video
Installation guide
After Extract, watch with your favorite Player.
English subtitle
Quality: 720p
Download Links
Size
1.57 GB
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