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This course aims to give students the tools and training to recognize convex optimization problems that arise in scientific and engineering applications, presenting the basic theory, and concentrating on modeling aspects and results that are useful in applications. Topics include convex sets, convex functions, optimality conditions, and duality theory. Addition to that, applications to signal processing, control, and analogue circuit design will be 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 needs 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
Introduction 00:10:00
Convex sets 00:30:00
Convex functions 00:40:00
Convex optimization problems 01:05:00
Duality 00:40:00
Approximation and fitting 00:25:00
Statistical estimation 00:20:00
Geometric problems 00:25:00
Filter design and equalization 00:40:00
Miscellaneous applications 00:30:00
Module 02
l1 methods for convex-cardinality problems 00:40:00
l1 methods for convex-cardinality problems (cont.) 00:30:00
Stochastic programming 00:30:00
Chance constrained optimization 00:30:00
Numerical linear algebra background 00:25:00
Unconstrained minimization 00:40:00
Equality constrained minimization 00:25:00
Interior-point methods 00:40:00
Disciplined convex programming and CVX 00:35:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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