PPT-Scheduling SDP Processing Pipelines –

A Combinatorial Optimisation Approach Chen Wu ICRAR In collaboration with DFMS team Agenda 2016 SKA Workshop on SDPampHPC 2 The scheduling problem and goal The combinatorial optimisation approach. CSP. Prasad . Raghavendra. University of Washington, Seattle. David . Steurer. ,. Princeton. University. (In Principle). Constraint Satisfaction Problem. A Classic Example : . Max-3-SAT. Given a . Efficient Algorithms and their Limits. Prasad . Raghavendra. University of Washington. Seattle. . Max 3 SAT. . Find an assignment that satisfies the maximum number of clauses.. Max 3. SAT. Max 2. and . Spectral Profile and Related Parameters. Prasad . Raghavendra. MSR New England. S. David . Steurer. Princeton University. Prasad . Tetali. Georgia Tech. joint work with. Graph . Expansion. d. -regular graph . Effectiveness and Limitations. Yuan Zhou. Computer Science Department. Carnegie Mellon University. 1. Combinatorial Optimization. Goal:. optimize an objective function of . n. 0-1 variables. Subject to: . Prepared By:. John Blair. Sean Donahue. Celeste Hoffman. Kimberly . Klinkers. Megan Slater. Green River . Basin Location. Green River Basin . Stratigraphic. Correlation Chart showing Study Map Units. Raghavendra. University of Washington. Seattle . Optimal Algorithms and . Inapproximability. Results for . Every CSP?. Constraint Satisfaction Problem. A Classic Example : . Max-3-SAT. Given a . 3-SAT. Max . Nanao. Automatic Processing – why?. +Rapid feedback to user on data quality. +Enables “value added” services. . +MR phasing. . +Ligand fitting. . +Automatic SAD. +QA for us. Page . 2. Automatic processing at ESRF, History. Semidefinite. Programming. Satyen. Kale . (Yahoo! Research). Joint work with. Sanjeev. . Arora. . (Princeton). Semidefinite. Programming. Semidefinite. Program (SDP):. find . X. . s.t.. . Haggai . Maron, . Nadav. . Dym. , . . Itay. . Kezurer. , . . Shahar. . Kovalsky. , . . Yaron. . Lipman. . Weizmann . Institute of Science. 1. Orthogonal.  .  .  .  . Orthogonal Procrustes Problem. Aram Harrow (MIT). Simons Institute 2014.1.17. a theorem. Let M. 2. R. +. m. £. n. .. Say that a set S. ⊆[n]. k. is δ-good if . ∃φ:[m]. k. .  S. such that ∀(j. 1,. …, j. k. )∈S, . f(k,δ):= max{ |S| : ∃S⊆[n]. Columbia . University. . Graph-Theoretic Algorithm for Arbitrary Polynomial Optimization Problems with Applications to Distributed Control, Power Systems, and Matrix Completion. . Joint work with. :. Madan Musuvathi. . Visiting Professor, UCLA . Principal Researcher, Microsoft Research. Mid-point feedback. Are you learning from the papers we are reading?. Do you find class discussions helpful?. Does preparing for the class presentation help? . (for Max Cut). Venkatesan. . Guruswami. Fields Institute Summer School. June 2011. (Slides borrowed from Prasad . Raghavendra. ). Dictatorship Test. Given a function . . F : {-1,1}. R. {-1,1}. Facts • . Observations • Problems • Solutions. February 2016. Greg Lander– President. glander@skippingstone.com. Atlanta Boston Houston Los Angeles Tokyo. www.SkippingStone.com. Table of Contents.