Application of Sub-Graph Isomorphism to Extract Reoccurring Structures from BPMN 2.0 Process Models
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Slide 1 Iterative Improvement Search Hill Climbing, Simulated Annealing, WALKSAT, and Genetic Algorithms Andrew W. Moore Professor School of Computer Science.
Point-and-Line Problems. Introduction Sometimes we can find an exisiting algorithm that fits our problem, however, it is more likely that we will have.
Randomized Algorithms 1. 2 Randomization Algorithmic design patterns. n Greed. n Divide-and-conquer. n Dynamic programming. n Network flow. n Randomization.
SDD Solvers: Bridging theory and practice Yiannis Koutis University of Puerto Rico, Rio Piedras joint with Gary Miller, Richard Peng Carnegie Mellon University.
1 Randomization Algorithmic design patterns. n Greed. n Divide-and-conquer. n Dynamic programming. n Network flow. n Randomization. Randomization. Allow.
Rohit Ray ESE 251. The goal of the Traveling Salesman Problem (TSP) is to find the most economical way to tour of a select number of “cities” with the.
Lattice-based Cryptography Oded Regev Tel-Aviv University Oded Regev Tel-Aviv University CRYPTO 2006, Santa Barbara, CA.
AI – Week 18 + 19 AI Planning – Plan Generation Algorithms: GraphPlan Lee McCluskey, room 2/09 Email [email protected]@hud.ac.uk
4-1.1 Security I – General principles © 2010 B. Wilkinson/Clayton Ferner. Spring 2010 Grid computing course. Modification date: Jan 27, 2010.
BioInformatics - Protein Structure Prediction Rajalingam Aravinthan Gad Abraham Summer Studentship(2003/2004) Under the supervision of Professor Heiko.
