PPT-Parameterized Complexity Analysis for the

Closest String with Wildcards CSW Problem Danny Hermelin Liat Rozenberg CPM Moscow June 2014 The Closest String problem Given a set of m strings each of length . Shantanu. . Dutt. ECE Dept.. UIC. Time Complexity. An algorithm time complexity is a function T(n) of problem size n that represents how much time the algorithm will take to complete its task.. Note that there could be more than one problem size parameter n, in which case we can denote the time complexity function as T(S), where S is the set of size parameters. E.g., for the shortest path problem on a graph G, we have 2 size parameters, n the # of vertices and e the # of edges (thus T(S) = T(. Shantanu. . Dutt. ECE Dept.. UIC. Time Complexity. An . algorithm’s . time complexity is a function T(n) of problem size n that represents how much time the algorithm will take to complete its task.. Part I – Basics, Kernels and Branching. Daniel Lokshtanov. Basics / Motivation. If . L. is NP-hard then there is no algorithm which solves . all. instances of . L. in polynomial time.. What about the . Tao Xie . North Carolina. State University. Nikolai Tillmann, . Peli de Halleux, . Wolfram Schulte. . Microsoft Research. Outline. Unit Testing. Parameterized Unit Testing (PUT). Mutation Analysis for PUT. The Basics. Bart . M. P. . Jansen. Insert. «. Academic. unit» . on every page:. 1 Go to the menu «Insert». 2 Choose: Date and time. 3 Write the name of your faculty or department in the field «Footer». Parameterization. Parameterization is used to change the value of any variable at run time.. Following can be parameterized in a script file:-.  . URL portion.  . Header portion.  . BODY portion. using Parameterized Program Equivalence. University of California, San Diego. Sudipta. . Kundu. Zachary . Tatlock. Sorin. Lerner. Compiler Correctness. Difficult to develop reliable compilers:. large, complex programs. By . Patricia Lane. Dalhousie University. Goal. : to illustrate how Dick Levins’ loop analysis is useful for analyzing complex systems using a marine ecosystem example. Rationale: we have one ocean, which is constantly under threat; we need to understand how perturbations affect ecological networks through a myriad of pathways and feedbacks. Dr. Jeyakesavan Veerasamy. jeyv@utdallas.edu. The University of Texas at Dallas, USA. Program running time. When is the running time (waiting time for user) noticeable/important?. Program running time – Why? . What is the best way to measure the time complexity of an algorithm?. - Best-case run time?. - Worst-case run time?. - Average run time?. Which should we try to optimize?. Best-Case Measures. How can we modify almost any algorithm to have a good best-case running time?. Reading: Chapter 2. 2. Complexity Analysis. Measures efficiency (time and memory) of algorithms and programs. Can be used for the following. Compare different algorithms. See how time varies with size of the input. PI: Cristiana Stan, George Mason University/COLA. Atlantic wind-shear and its relationship with ENSO. S. ummer precipitation in the . eastern . U.S. . . Acknowledgement: . Xiaojie. Zhu and Li . Xu. Department of Computer Science.  . University of Crete. Introductory Lecture on Complex Systems. . . Prof. Maria Papadopouli. . Each column contains three examples of systems consisting of the same components (from left to right: molecules, cells, people) but with . Ashish Agarwal. Shannon Chen. The University of Texas . at Austin. Rahul . Tikekar. Ririko. Horvath. Larry May . IRS, RAS. Advisory Roles: . Robert . Hanneman. ( UC Riverside), Lillian Mills (UT Austin).