Talk:Algorithms Seminar/Fall11
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5) Gradient Descent / Hill Climbing: | 5) Gradient Descent / Hill Climbing: | ||
| - | http://webdocs.cs.ualberta.ca/~sutton/book/8/node3.html | + | [http://webdocs.cs.ualberta.ca/~sutton/book/8/node3.html 1] |
| - | http://www.ri.cmu.edu/pub_files/pub1/baird_leemon_1999_1/baird_leemon_1999_1.pdf | + | [http://www.ri.cmu.edu/pub_files/pub1/baird_leemon_1999_1/baird_leemon_1999_1.pdf 2] |
6) Alternating Optimization, EM like methods: | 6) Alternating Optimization, EM like methods: | ||
| - | http://geomblog.blogspot.com/2008/01/important-heuristics-alternating.html | + | [http://geomblog.blogspot.com/2008/01/important-heuristics-alternating.html 1] |
| - | http://www.springerlink.com/content/c21tfr6cxmjbmq3r/ | + | [http://www.springerlink.com/content/c21tfr6cxmjbmq3r/ 2] |
| - | http://dl.acm.org/citation.cfm?id=964886 | + | [http://dl.acm.org/citation.cfm?id=964886 3] |
7) Swarm Intelligence based methods: | 7) Swarm Intelligence based methods: | ||
| - | http://www.springerlink.com/content/q528q1743224q233/ | + | [http://www.springerlink.com/content/q528q1743224q233/ 1] |
| - | + | [www.idsia.ch/~luca/aco2004.pdf Ant colony optimization] | |
| - | + | [http://www.inderscience.com/filter.php?aid=22775 Intelligent Water Drops] | |
8) Metropolis-Hastings algorithm: | 8) Metropolis-Hastings algorithm: | ||
| - | http://elsa.berkeley.edu/reprints/misc/understanding.pdf | + | [http://elsa.berkeley.edu/reprints/misc/understanding.pdf |
| - | http://fedc.wiwi.hu-berlin.de/xplore/ebooks/html/csa/node27.html | + | [http://fedc.wiwi.hu-berlin.de/xplore/ebooks/html/csa/node27.html |
9) Nelder-Mead Method: | 9) Nelder-Mead Method: | ||
| - | http://math.fullerton.edu/mathews/n2003/neldermead/NelderMeadProof.pdf | + | [http://math.fullerton.edu/mathews/n2003/neldermead/NelderMeadProof.pdf |
11) Monkey Search: | 11) Monkey Search: | ||
| - | www.ise.ufl.edu/cao/Book_DMSAOB/009mucherino.pdf | + | [www.ise.ufl.edu/cao/Book_DMSAOB/009mucherino.pdf |
12) Firefly Algorithm: | 12) Firefly Algorithm: | ||
| - | http://groups.csail.mit.edu/mac/projects/amorphous/HC11/fcresear.html | + | [http://groups.csail.mit.edu/mac/projects/amorphous/HC11/fcresear.html |
| - | http://bluescarni.info/documents/lukasik_firefly.pdf | + | [http://bluescarni.info/documents/lukasik_firefly.pdf |
| - | http://arxiv.org/abs/1003.1464 | + | [http://arxiv.org/abs/1003.1464 |
13) No free lunch in search and optimization: | 13) No free lunch in search and optimization: | ||
| - | http://ti.arc.nasa.gov/m/profile/dhw/papers/78.pdf | + | [http://ti.arc.nasa.gov/m/profile/dhw/papers/78.pdf |
14) Heuristics for NP-hard problems: | 14) Heuristics for NP-hard problems: | ||
| - | + | [www.cs.ubc.ca/~hutter/previous-earg/EmpAlgReadingGroup/Sau02.pdf Heuristic SAT] | |
| - | www.cs.ubc.ca/~hutter/previous-earg/EmpAlgReadingGroup/Sau02.pdf | + | [www.academic.marist.edu/~jzbv/algorithms/approximatetspalgorithms.pdf Heuristic TSP] |
| - | + | [http://www-e.uni-magdeburg.de/mertens/TSP/ TSP Animation] | |
| - | + | [http://www.cs.amherst.edu/~ccm/cs34/papers/tabuveh2661622.pdf TABUROUTE] | |
| - | www.academic.marist.edu/~jzbv/algorithms/approximatetspalgorithms.pdf | + | [http://dl.acm.org/citation.cfm?id=1224164 Heuristic Vehicle Routing Problem] |
| - | + | [http://www.iro.umontreal.ca/~gendron/PLU6000/chap5ca99b.ps Vehicle Routing 2] | |
| - | + | [http://www.diku.dk/hjemmesider/ansatte/sropke/Papers/PHDThesis.pdf Routing-thesis] | |
| - | + | [http://home.dei.polimi.it/malucell/papers/stellac.pdf Heuristic Set Cover] | |
| - | + | [http://dl.acm.org/citation.cfm?id=89230 Heuristic Task Scheduling] | |
| - | + | ||
| - | http://dl.acm.org/citation.cfm?id=1224164 | + | |
| - | www.iro.umontreal.ca/~gendron/PLU6000/chap5ca99b.ps | + | |
| - | www.diku.dk/hjemmesider/ansatte/sropke/Papers/PHDThesis.pdf | + | |
| - | + | ||
| - | + | ||
| - | http://home.dei.polimi.it/malucell/papers/stellac.pdf | + | |
| - | + | ||
| - | + | ||
| - | http://dl.acm.org/citation.cfm?id=89230 | + | |
Revision as of 18:17, 18 August 2011
Possible Topics:
1) Cuckoo Search: 1
2) Simulated Annealing: 1
3) SVM: 1
4) Evolutionary Algorithms: 1
5) Gradient Descent / Hill Climbing: 1 2
6) Alternating Optimization, EM like methods: 1 2 3
7) Swarm Intelligence based methods: 1 [www.idsia.ch/~luca/aco2004.pdf Ant colony optimization] Intelligent Water Drops
8) Metropolis-Hastings algorithm: [http://elsa.berkeley.edu/reprints/misc/understanding.pdf [http://fedc.wiwi.hu-berlin.de/xplore/ebooks/html/csa/node27.html
9) Nelder-Mead Method: [http://math.fullerton.edu/mathews/n2003/neldermead/NelderMeadProof.pdf
11) Monkey Search: [www.ise.ufl.edu/cao/Book_DMSAOB/009mucherino.pdf
12) Firefly Algorithm: [http://groups.csail.mit.edu/mac/projects/amorphous/HC11/fcresear.html [http://bluescarni.info/documents/lukasik_firefly.pdf [http://arxiv.org/abs/1003.1464
13) No free lunch in search and optimization: [http://ti.arc.nasa.gov/m/profile/dhw/papers/78.pdf
14) Heuristics for NP-hard problems: [www.cs.ubc.ca/~hutter/previous-earg/EmpAlgReadingGroup/Sau02.pdf Heuristic SAT] [www.academic.marist.edu/~jzbv/algorithms/approximatetspalgorithms.pdf Heuristic TSP] TSP Animation TABUROUTE Heuristic Vehicle Routing Problem Vehicle Routing 2 Routing-thesis Heuristic Set Cover Heuristic Task Scheduling
Related areas:
Metaheuristics: Search within a search space of problem solutions. Hyper-heuristics: Search within a search space of heuristics.
Talks:
Effective Heuristics for NP-Hard Problems, Richard Karp
Books:
J Pearl, Heuristics: intelligent search strategies for computer problem solving. CR Reeves, Modern Heuristic Techniques for Combinatorial Problems. Eric Bonabeau, Marco Dorigo, Guy Theraulaz, Swarm intelligence: from natural to artificial systems. Yang, Nature-Inspired Metaheuristic Algorithms
Other Links: Heuristic Search: http://www.cs.cf.ac.uk/Dave/AI2/node23.html Combinatorial Optimization: http://www.ida.liu.se/~zebpe/heuristic/ NP Hard problems and heuristics: http://www.cs.uky.edu/~lewis/cs-heuristic/text/contents.html Analysis of heuristics: Thesis: Eran Ofek, Rigorous Analysis of Heuristics for NP-hard Problems
