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This course introduces the principal algorithms for linear, network, nonlinear, dynamic optimization and optimal control. Emphasis is on methodology and the underlying mathematical structures. Topics include such as the simplex method, network flow methods, bound and cutting plane methods for discrete optimization. In addition to that, interior point methods for convex optimization, Newton’s method, dynamic programming and optimal control methods are also discussed in this course.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer need to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


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Course Credit: MIT

Course Curriculum

Module 01
Applications of linear optimization 00:14:00
Geometry of linear optimization 00:19:00
Simplex method I 00:10:00
Simplex method II 00:20:00
Duality theory I 00:20:00
Duality theory II 00:19:00
Sensitivity analysis 00:20:00
Robust optimization 00:17:00
Large scale optimization 00:20:00
Network flows I 00:18:00
Network flows II 00:18:00
Applications of discrete optimization 00:16:00
Module 02
Branch and bound and cutting planes 00:16:00
Lagrangean methods 00:18:00
Heuristics and approximation algorithms 00:20:00
Dynamic programming 00:20:00
Applications of nonlinear optimization 00:12:00
Optimality conditions and gradient methods 00:17:00
Line searches and Newton’s method 00:19:00
Conjugate gradient methods 00:19:00
Affine scaling algorithm 00:15:00
Interior point methods 00:19:00
Semidefinite optimization I 00:16:00
Semidefinite optimization II 00:20:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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