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Deterministic Optimization

Georgia Institute of Technology via edX

4 Reviews 34 students interested

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Overview

This course blends optimization theory and computation and its teachings can be applied to modern data analytics, economics, and engineering. Organized across four modules, it takes learners through basic concepts, models, and algorithms in linear optimization, convex optimization, and integer optimization. 

The first module of the course is a general overview of key concepts in linear algebra, calculus, and optimization. The second module of the course is on linear optimization, covering modeling techniques with many applications, basic polyhedral theory, simplex method, and duality theory. The third module is on convex conic optimization, which is a significant generalization of linear optimization. The fourth and final module focuses on integer optimization, which augments the previously covered optimization models with the flexibility of integer decision variables.

Syllabus

Week 1
  • Module 1: Introduction
  • Module 2: Illustration of the Optimization Problems
Week 2
  • Module 3: Review of Mathematical Concepts
  • Module 4: Convexity
Week 3
  • Module 5: Outcomes of Optimization
  • Module 6: Optimality Certificates
Week 4
  • Module 7: Unconstrained Optimization: Derivate Based
  • Module 8: Unconstrained Optimization: Derivative Free
Week 5
  • Module 9: Linear Optimization Modeling – Network Flow Problems
  • Module 10: Linear Optimization Modeling – Electricity Markets
Week 6
  • Module 11: Linear Optimization Modeling – Decision-Making Under Uncertainty
  • Module 12: Linear Optimization Modeling – Handling Nonlinearity 
Week 7
  • Module 13: Geometric Aspects of Linear Optimization
  • Module 14: Algebraic Aspect of Linear Optimization
Midterm

Week 8
  • Module 15: Simplex Method in a Nutshell
  • Module 16: Further Development of Simplex Method
Week 9
  • Module 17: Linear Programming Duality
  • Module 18: Robust Optimization
Week 10
  • Module 19: Nonlinear Optimization Modeling – Approximation and Fitting
  • Module 20: Nonlinear Optimization Modeling – Statistical Estimation
Week 11
  • Module 21: Convex Conic Programming – Introduction
  • Module 22: Second-Order Conic Programming – Examples
Week 12
  • Module 23: Second-Order Conic Programming – Advanced Modeling
  • Module 24: Semi-definite Programming – Advanced Modeling
Week 13
  • Module 25: Discrete Optimization: Introduction
  • Module 26: Discrete Optimization: Modeling with binary variables - 1
Week 14
  • Module 27: Discrete Optimization: Modeling with binary variables – 2
  • Module 28: Discrete Optimization: Modeling exercises
Week 15
  • Module 29: Discrete Optimization: Linear programming relaxation
  • Module 30: Discrete Optimization: Solution methods

Taught by

Andy Sun

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Reviews for edX's Deterministic Optimization
3.5 Based on 4 reviews

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  • 1
Anonymous
3.0 11 months ago
Anonymous completed this course.
This course (run in early 2018) broke the enrollment clause stating "Audit this course for free and have complete access to all the course material, activities, tests, and forums". There was no midterm and final exams for Audit students. Therefore I was not able to obtain even a symbolic passing grade, despite completing all assignments that were provided to me.

Moreover the staff did not provide responses to the issues reported by the students on the forums. The issues included:

- duplicate video uploads (addressed)

- missing video parts in Week 4 Modu…
Was this review helpful to you? Yes
Stiven S
4.0 10 months ago
Stiven completed this course and found the course difficulty to be medium.
The course is an introduction to deterministic optimization, the topics covered range from non-constrained optimization (the most basic one) to basic algorithms for integer programming, central topics like convexity and applications are also included. Mostly Python is used for solving the problems making used of several open source libraries, MATLAB was briefly mentioned but in my opinion Python is better for this field. The quality of the lectures is very good, and the peer review assessments allow students to have a sort of personalized feedback.

I think this was the first run of the course, so there is room for improvement in a few aspects. The communication between the staff and the students is very poor, we almost never got answer from them, a number of topics in the syllabus were not presented and the order of the topics was changed in some occasions, I was particularly disappointed during the last modules.
Was this review helpful to you? Yes
Salmo S
3.0 10 months ago
Salmo completed this course and found the course difficulty to be medium.
This course was a basic course to understand linear optimization and methods to reformulate large linear or non-linear models to a solvable/linear model. The course assignments are half theoretical and half computational using Python or MATLAB. I wish it had a second part working more with non-linear models and available tools for nonlinear optimization in Python.

Major problems of the course are lack of STAFF communications on changes/updates and the management of the errors happened by homework submissions and corrections, which were at times nerve racking. Also the syllabus could be more accurate and organized.
Was this review helpful to you? Yes
Mohamed C
4.0 a year ago
by Mohamed is taking this course right now, spending 8 hours a week on it and found the course difficulty to be medium.
The introductory text says there 4 modules, but the syllabus shows that there are 14 modules.

Please correct or explain the discrepancy.
Was this review helpful to you? Yes
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