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(Complete Item Description)
- Abstract:
A first-year graduate course in algorithms. Emphasizes fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Data structures. Network flows. Linear programming. Computational geometry. Approximation algorithms.
- Subject:
- Science and Technology
- Grade Level:
- Post-secondary
- Collection:
-
MIT OpenCourseWare
Remix and Share
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(Complete Item Description)
- Abstract:
A first-year graduate course in algorithms. Emphasizes fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Data structures. Network flows. Linear programming. Computational geometry. Approximation algorithms. Alternate years.
- Subject:
- Science and Technology
- Grade Level:
- Post-secondary
- Collection:
-
MIT OpenCourseWare
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(Complete Item Description)
- Abstract:
This course is a first-year graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Techniques to be covered include amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms. Domains include string algorithms, network optimization, parallel algorithms, computational geometry, online algorithms, external memory, cache, and streaming algorithms, and data structures.
- Subject:
- Science and Technology
- Grade Level:
- Post-secondary
- Collection:
-
MIT OpenCourseWare
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(Complete Item Description)
- Abstract:
" This is a graduate course on the design and analysis of algorithms, covering several advanced topics not studied in typical introductory courses on algorithms. It is especially designed for doctoral students interested in theoretical computer science."
- Subject:
- Mathematics and Statistics, Science and Technology
- Grade Level:
- Post-secondary
- Collection:
-
MIT OpenCourseWare
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(Complete Item Description)
- Abstract:
Thorough treatment of linear programming and combinatorial optimization. Topics include network flow, matching theory, matroid optimization, and approximation algorithms for NP-hard problems. 18.310 helpful but not required.
- Subject:
- Mathematics and Statistics
- Grade Level:
- Post-secondary
- Collection:
-
MIT OpenCourseWare
