PPT-Parameterized Complexity

Part I Basics Kernels and Branching Daniel Lokshtanov Basics Motivation If L is NPhard then there is no algorithm which solves all instances of L in polynomial time What about the . 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.. Ian Bertolacci – Colorado State University. Catherine . Olschanowsky. – Colorado State University. Ben . Harshbarger. – Cray Inc.. Bradford L. . Chamerlain. – Cray Inc.. David G. . Wonnacott. English Language Arts Common Core State Standards. Illinois State Board of Education . English Language Arts Content Specialists. Hosted by Erik Iwersen, June, 2012. . Text Complexity. . “. The Common Core Standards hinge on students encountering appropriately complex texts at each grade level in order to develop the mature language skills and the conceptual knowledge they need for success in school and life. 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 . 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. Dr . W. ale Fawehinmi . . Centre . for Public Policy Alternatives (CPPA. ), Lagos June . 18. th. 2014. . Profile: Dr Fawehinmi . BDS. (University of Lagos, Nigeria; 1983). MBA. . (University of Leicester, UK; 2010). Why don’t languages evolve toward efficiency?. “As they evolve, things become more efficient.”. Efficient operations, tools, methods, etc. should drive out those that are difficult and costly.. Amanda Jackson. Fronde Systems Group Ltd. Session Code: ARC206. Presentation Outline (hidden slide):. Title: Managing Complexity in a Software plus Services World. Technical Level: All levels. Intended Audience: Developers, Enterprise Architects. Lecture 1: . Intro; Turing machines; . Class P and NP . . . Indian Institute of Science. About the course. Computational complexity attempts . to classify computational . problems. Theophilus Benson (tbenson@cs.wisc.edu). Aditya Akella (akella@cs.wisc.edu). David A Maltz (dmaltz@microsoft.com). Enterprise Networks. Intricate logical and physical topologies. Diverse network devices. Wing Lam (U. Illinois). In collaboration with . Siwakorn. . Srisakaokul. , Blake Bassett, . Peyman. . Mahdian. and Tao . Xie. (U. Illinois). Nikolai Tillman, Jonathan de . Halleux. . (Microsoft Research) . 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. 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. Lijie. Chen. MIT. Today’s Topic. Background. . What is Fine-Grained Complexity?. The Methodology of Fine-Grained Complexity. Frontier: Fine-Grained Hardness for Approximation Problems. The Connection.