In keeping with the tradition of the last years, the Readings in Optimization seminar will focus on an advanced topic of interest to a portion of the MIT optimization community randomized methods for deterministic optimization. The course will cover some recent papers on this topic, many by MIT faculty, as well as some older papers from the existing literature that is only now receiving attention.
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
|Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming
|Simple Polynomial-Time Rescaling Algorithm for Solving Linear Programs
|Las Vegas Algorithms for Linear and Integer Programming When the Dimension Is Small(i)
|Las Vegas Algorithms for Linear and Integer Programming When the Dimension Is Small(ii)
|A Subexponential Randomized Simplex Algorithm
|Minimization by Random Search Techniques
|Pure Adaptive Search in Global Optimization(i)
|Pure Adaptive Search in Global Optimization(ii)
|Submit Your Assignment
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