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
|Convex optimization problems||01:05:00|
|Approximation and fitting||00:25:00|
|Filter design and equalization||00:40:00|
|l1 methods for convex-cardinality problems||00:40:00|
|l1 methods for convex-cardinality problems (cont.)||00:30:00|
|Chance constrained optimization||00:30:00|
|Numerical linear algebra background||00:25:00|
|Equality constrained minimization||00:25:00|
|Disciplined convex programming and CVX||00:35:00|
|Submit Your Assignment||00:00:00|
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