MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 52 lectures (8h 20m) | Size: 2.63 GB
This course is designed for complete bners to mathematical optimization.
Learn how to mathematically formulate business problems and find their optimal solutions using GAMS and Pyomo (Python)
Mathematical optimization
Linear programming
Integer programming
Nonlinear programming
Hands-on coding experience in GAMS
Hands-on coding experience in Pyomo (Python)
There are no coding prerequisites either, as we go through the functions and syntaxes in GAMS and Pyomo in detail. We instruct you on the and demo license installation for GAMS. Pyomo is an open source package which we use Google Colaboratory to run. Therefore, all you need is a functional Google account, and you are ready to get started on this introductory journey to optimization!
This introductory course to optimization in GAMS and Pyomo (Python) contains 4 modules, namely,
Linear programming
Nonlinear programming
Mixed Integer Linear Programming, and
Mixed-Integer Nonlinear Programming
In each module, we aim to teach you the basics of each type of optimization through three different illustrative examples from different areas of science, eeering, and management. Using these examples, we aim to gently introduce you to coding in two environments commonly used for optimization, GAMS and Pyomo. GAMS is a licensed software, for which we use a demo license in this course. Pyomo is an open-source package in Python, which we use Google Colaboratory to run. As we proceed through the different examples in each module, we also introduce different functionalities in GAMS and Python, including data import and export.
At the end of this course, you will be able to,
Read a problem statement and build an optimization model
Be able to identify the objective function, decision variables, constraints, and parameters
Code an optimization model in GAMS
Define sets, variables, parameters, scalars, equations
Use different solvers in GAMS
Leverage the NEOS server for optimization
Import data from text, gdx, and spreadsheet files
Export data to text, gdx, and spreadsheet files
Impose different variable ranges, and bounds
Code an optimization model in Pyomo
Define models, sets, variables, parameters, constraints, and objective function
Use different solvers in Pyomo
Leverage the NEOS server for optimization
Import data from text, gdx, and spreadsheet files
Export data to text, gdx, and spreadsheet files
Impose different variable ranges, and bounds
We have designed this course to be accessible to students and professionals in various disciplines including, but not limited to, operations research, eeering, science, and management. Hence, we have chosen illustrative examples for each module within the course from different disciplines such as production scheduling, chal and electrical eeering, geometry, etc. For each example, we will go through the problem statement in detail before proceeding to coding in GAMS and Python, so rest assured that you can follow through regardless of your field of learning and work.
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