CS 740: Algorithms, Complexity and the Theory of Computability
2008
- 114Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage114
- Downloads108
- Abstract Views6
Syllabus Description
The objective of this course is to use the formal algorithmic system provided by Turing machines as a tool to analyze the complexity of decision and optimization problems and the algorithms that solve them. The topics to be covered include• the definition of the time and space complexity of a deterministic algorithm• the classes of deterministic polynomial and non-polynomial time languages• the complexity of nondeterministic algorithms• the P=NP question (relationship between solvability by deterministic and nondeterministic polynomial time algorithms)• the implications of a solution to the P=NP question• NP completeness and examples of NP complete problems• classes of NP complete problems• techniques for approximate solutions of NP complete problems
Bibliographic Details
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