<--- Back to Details
First PageDocument Content
Algorithms / Combinatorial optimization / Probabilistic complexity theory / Randomized rounding / Linear programming relaxation / Randomized algorithm / Mathematics / Applied mathematics / Theoretical computer science
Date: 2012-11-29 13:39:05
Algorithms
Combinatorial optimization
Probabilistic complexity theory
Randomized rounding
Linear programming relaxation
Randomized algorithm
Mathematics
Applied mathematics
Theoretical computer science

Approximation Algorithms (ADM III)

Add to Reading List

Source URL: www.coga.tu-berlin.de

Download Document from Source Website

File Size: 111,63 KB

Share Document on Facebook

Similar Documents

A  Family  of  Provably  Correct  Algorithms   for  Exact  Triangle  Coun;ng       Ma=hew  Lee,  Tze  Meng  Low   Correctness  2017    

DocID: 1xVUG - View Document

Sorting algorithms / Order theory / Mathematics / Combinatorics / Quicksort / Shellsort / Insertion sort / Merge sort / Adaptive sort / Factorial / Time complexity / Heapsort

Theoretical Computer Science–40 www.elsevier.com/locate/tcs Presorting algorithms: An average-case point of view

DocID: 1xVR3 - View Document

Computer arithmetic / Arithmetic / Computing / Binary arithmetic / Theory of computation / Data types / IEEE standards / Decimal64 floating-point format / IEEE 754 / Double-precision floating-point format / cole normale suprieure de Lyon / Algorithm

Formal Correctness of Comparison Algorithms between Binary64 and Decimal64 Floating-point Numbers Arthur Blot ENS Lyon, France NSV, July 22-23, 2017

DocID: 1xVvl - View Document

Edsger W. Dijkstra / Network theory / Shortest path problem / Constructible universe / Total least squares

Theory and Techniques for Synthesizing a Family of Graph Algorithms Srinivas Nedunuri William R. Cook

DocID: 1xVkB - View Document

Genetic algorithms / Evolutionary algorithms / Artificial intelligence / Applied mathematics / Cybernetics / Mathematical optimization / Mathematics / Genetic programming / Algorithm / Crossover

Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms Ari Juels Department of Computer Science

DocID: 1xVfc - View Document