Areas:"Electrical Engineering and Computer Science"
continent:"North America"
language:"English"
license:"Creative Commons Attribution-NonCommercial-ShareAlike 3.0"
resourceType:"ocw"
university_name:"Massachusetts Institute of Technology"
university_name:"Massachusetts Institute of Technology"
university_name:"Massachusetts Institute of Technology"
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Published by: Massachusetts Institute of Technology | Language: English
Published by: Massachusetts Institute of Technology | Language: English
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This course will focus on fundamental subjects in (deterministic) optimization, connected through the themes of convexity, geometric multipliers, and duality. The aim is to develop the core analytical and computational issues of continuous optimization, duality, and saddle point theory using a handful of unifying principles that can be easi
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Tag(s):
- electrical engineering and computer science
- convexity
- optimization
- geometric duality
- lagrangian duality
- fenchel duality
- cone programming
- semidefinite programming
- subgradients
- constrained optimization
- gradient projection
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Published by: Massachusetts Institute of Technology | Language: English
Published by: Massachusetts Institute of Technology | Language: English
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Data structures play a central role in modern computer science. You interact with data structures much more often than with algorithms (think of Google, your mail server, and even your network routers). In addition, data structures are essential building blocks in obtaining efficient algorithms. This course will cover major results and curr
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Tag(s):
- electrical engineering and computer science
- dynamic optimality
- geometric data structures
- strings
- integers
- dictionaries
- dynamic graphs
- temporal data structures
- external memory
- cache-oblivious
- succinct data structures
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Published by: Massachusetts Institute of Technology | Language: English
Published by: Massachusetts Institute of Technology | Language: English
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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); graph algorithms (minimum spanning trees, shortest paths, minimu
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Tag(s):
- electrical engineering and computer science
- randomized algorithms
- algorithms
- efficient in time and space
- randomization
- computational problems
- data structures
- graph algorithms
- optimization
- geometry
- markov chains
- sampling
- estimation
- geometric algorithms
- parallel and distributed algorithms
- parallel and ditributed algorithm
- parallel and distributed algorithm
- random sampli
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