This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include randomized computation data structures (hash tables, skip lists). It also discusses geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension), approximate counting, parallel algorithms, online algorithms, and tools for probabilistic analysis of algorithms.
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
|Introduction to Randomized Algorithms||00:10:00|
|Min-Cut, Complexity Theory, Game Tree Evaluation||00:10:00|
|Adelman’s Theorem, Game Theory, Lower Bounds||00:10:00|
|Coupon Collecting, Stable Marriage, Markov Inequality||00:10:00|
|Chebyshev, Two Point Sampling, Chernoff||00:10:00|
|Median Finding, Routing||00:10:00|
|Probabilistic Method, Expanders, Wiring, MAX SAT||00:10:00|
|Method of Conditional Probabilities and Expectations, Fingerprinting||00:10:00|
|Hashing, Perfect Hash Families, Freivald’s Technique||00:10:00|
|Fingerprints by Polynomials, Perfect Matching, Hashing||00:10:00|
|Maximal Independent Sets||00:10:00|
|Minimum Spanning Trees||00:10:00|
|Polling, Minimum Cut, Transitive Closure||00:10:00|
|Estimating Min-Cut Size||00:10:00|
|UTS, Eigenvalue Analysis, Expanders||00:10:00|
|Expander based Pseudo-Random Generator||00:10:00|
|Sampling with Markov Chains, Coupling||00:10:00|
|Randomized Incremental Construction||00:10:00|
|Trapezoidal Decomposition, Treaps||00:10:00|
|Submit Your Assignment||00:00:00|
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