PPT-Global Optimization General issues in global optimization

Classification of algorithms The DIRECT algorithm Divided rectangles Exploration and Exploitation as biobjective optimization Application to High Speed Civil Transport Global optimization issues. . Dianzi. . Liu, . V. assili. . V. . Toropov. , . Osvaldo M. . Querin. University . of Leeds. . Content. Introduction. Topology Optimisation. Parametric Optimisation. CSE 8330. Instructor: . Dr.Margaret. H. Dunham. Presenter: . Akshaya. . Aradhya. Introduction. Query optimization in XML databases. Query optimization in Parallel databases. Comparison. Conclusion and Future work. . Kwangsoo. Han, Andrew B. Kahng, . Jongpil. Lee, . Jiajia Li. and Siddhartha Nath. VLSI CAD LABORATORY, . UC. San Diego. Outline. Motivation. Related Work. Our Optimization Framework. Experimental Setup and Results. Kepler. Zehuan Wang. zehuan@nvidia.com. Fundamental Optimization. Optimization Overview. GPU . a. rchitecture. Kernel optimization. Memory optimization. Latency optimization. Instruction . optimization. GLOBAL OPTIMIZATION USING DERRINGER’S DESIRABILITY FUNCTION: ENANTIOSELECTIVE DETERMINATION OF KETOPROFEN IN FORMULATIONS AND IN BIOLOGICAL MATRICES T. Sivakumar, R. Manavalan, and K. Valliappa Scheduling and . Placing in Reconfigurable Systems. Fabrizio. . Ferrandi. , . PierLuca. . Lanzi. , . Christian Pilato. , Donatella . Sciuto. Politecnico. di Milano – Dip. di . Elettronica. , . Informazione. Flow. Manuel Ruiz – RTE – manuel.ruiz@rte-france.com. 1. 2. A . small. . example. . The power system network is a graph described . using . buses (nodes) and branches (directed edges). . Power flow computations aim at providing a power and voltage distribution of a power system network given . Qifeng. Chen. Stanford University. Vladlen. . Koltun. Intel Labs. Optical flow. Motion field between two image frames. Optical flow. Motion field between two image frames. Image 1. Image 2. optical flow. Univariate. optimization. x. f. (x). Key Ideas. Critical points. Direct methods. Exhaustive search. Golden section search. Root finding algorithms. Bisection. [More next time]. Local vs. global optimization. Design . Using . DiscoverSim. ™. John Noguera. CTO & Co-Founder . SigmaXL, Inc.. www.SigmaXL.com. October 12, . 2011. Copyright © 2011, SigmaXL, Inc.. 2. Variation Reduction and Robust Design Using . The EGO algorithm. 1. Introduction to optimization with surrogates. Based on cycles. Each consists of sampling design points by simulations, fitting surrogates to simulations and then optimizing an objective.. Ranga Rodrigo. April 6, 2014. Most of the sides are from the . Matlab. tutorial.. 1. Introduction. Global Optimization Toolbox provides methods that search for global solutions to problems that contain multiple maxima or minima. . J. McCalley. 1. Real-time. Electricity markets and tools. Day-ahead. SCUC and SCED. SCED. Minimize f(. x. ). s. ubject to. h. (. x. )=. c. g. (. x. ). <. . b. BOTH LOOK LIKE THIS. SCUC: . x. contains discrete & continuous variables.. Non-convex optimization. All loss-functions that are not convex: not very informative.. Global optimality: too strong. Weaker notions of optimality?. What is a saddle point?. Different kinds of critical/stationary points.