PPT-Kernel Bounds for Structural Parameterizations of

Pathwidth Bart M P Jansen Joint work with Hans L Bodlaender amp Stefan Kratsch July 6th 2012 SWAT 2012 Helsinki What is pathwidth 2 What is pathwidth Measure of how pathlike a graph is. Indeed developing bounds on the per formance of procedures can give complementary insights By exhibiting fundamental limits of performance perhaps over restricted classes of estimators it is possible to guarantee that an a lgorithm we have developed IK. November 2014. Instrument Kernel. 2. The Instrument Kernel serves as a repository for instrument specific information that may be useful within the SPICE context.. Always included:. Specifications for an instrument’s field-of-view (FOV) size, shape, and orientation. Shubhangi. . Saraf. Rutgers University. Based on joint works with . Albert Ai, . Zeev. . Dvir. , . Avi. . Wigderson. Sylvester-. Gallai. Theorem (1893). v. v. v. v. Suppose that every line through . 2 - . Calculations. www.waldomaths.com. Copyright © . Waldomaths.com. 2010, all rights reserved. Two ropes, . A. and . B. , have lengths:. A = . 36m to the nearest metre . B = . 23m to the nearest metre.. Shubhangi. . Saraf. Rutgers University. Based on joint works with . Albert Ai, . Zeev. . Dvir. , . Avi. . Wigderson. Sylvester-. Gallai. Theorem (1893). v. v. v. v. Suppose that every line through . Arul Asirvatham, Emil Praun . (University of Utah). Hugues Hoppe . (Microsoft Research). 2. Consistent Spherical Parameterizations. 3. Parameterization. Mapping from a domain (plane, sphere, simplicial complex) to surface. 0.2 0.4 0.6 0.8 1.0 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 kernel(b) kernel(c) kernel(d) (a)blurredimage(b)no-blurredimage0.900.981.001.021.10 (5.35,3.37)(4.80,3.19)(4.71,3.22)(4.93,3.23)(5.03,3.22 Set:. Hitting . Paths in Graphs Using . 2-SAT. Bart M. P. . Jansen. June 19th, WG 2015, Munich, Germany. The . Hitting Set. Problem. Input:. . A family . of subsets of a finite universe . , and an integer . unseen problems. David . Corne. , Alan Reynolds. My wonderful new algorithm, . Bee-inspired Orthogonal Local Linear Optimal . Covariance . K. inetics . Solver. Beats CMA-ES on 7 out of 10 test problems !!. approximate membership. dynamic data structures. Shachar. Lovett. IAS. Ely . Porat. Bar-. Ilan. University. Synergies in lower bounds, June 2011. Information theoretic lower bounds. Information theory. Arul Asirvatham, Emil Praun . (University of Utah). Hugues Hoppe . (Microsoft Research). 2. Consistent Spherical Parameterizations. 3. Parameterization. Mapping from a domain (plane, sphere, simplicial complex) to surface. A combinatorial approach to P . vs. NP. Shachar. Lovett. Computation. Input. Memory. Program . Code. Program code is . constant. Input has . variable length (n). Run time, memory – grow with input length. Kernel Structure and Infrastructure David Ferry, Chris Gill, Brian Kocoloski CSE 422S - Operating Systems Organization Washington University in St. Louis St. Louis, MO 63130 1 Kernel vs. Application Coding dynamic data structures. Shachar. Lovett. IAS. Ely . Porat. Bar-. Ilan. University. Synergies in lower bounds, June 2011. Information theoretic lower bounds. Information theory. is a powerful tool to prove lower bounds, e.g. in data structures.