PPT-Evaluating optimization algorithms: bounds on the performa

unseen problems David Corne Alan Reynolds My wonderful new algorithm Beeinspired Orthogonal Local Linear Optimal Covariance K inetics Solver Beats CMAES on 7 out of 10 test problems . for Linear Algebra and Beyond. Jim . Demmel. EECS & Math Departments. UC Berkeley. 2. Why avoid communication? (1/3). Algorithms have two costs (measured in time or energy):. Arithmetic (FLOPS). Communication: moving data between . Regrets and . Kidneys. Intro to Online Stochastic Optimization. Data revealed over time. Distribution . of future events is known. Under time constraints. Limits amount of . sampling/simulation. Solve these problems with two black boxes:. Combinatorial and Graph Algorithms. Welcome!. CS5234 Overview. Combinatorial & Graph Algorithms. http://. www.comp.nus.edu.sg/~cs5234/. Instructor: . Seth Gilbert. Office: . COM2-204. Office hours: . for Geometry Processing. Justin Solomon. Princeton University. David . Bommes. RWTH Aachen University. This Morning’s Focus. Optimization.. Synonym(-. ish. ):. . Variational. methods.. This Morning’s Focus. relaxations. via statistical query complexity. Based on:. V. F.. , Will Perkins, Santosh . Vempala. . . On the Complexity of Random Satisfiability Problems with Planted . Solutions.. STOC 2015. V. F.. Optimization Algorithms. Welcome!. CS4234 . Overview. Optimization Algorithms. http://. www.comp.nus.edu.sg/. ~gilbert/CS4234. Instructor: . Seth Gilbert. Office: . COM2. -323. Office hours: . by appointment. Collin . Bezrouk. 2-24-2015. Discussion Reference. Some of this material comes from . Spacecraft Trajectory Optimization. (Ch. 7) by Bruce Conway.. Optimization Problem Setup. Optimization problems require the following:. Lower Bounds, and Pseudorandomness. Igor Carboni Oliveira. University of Oxford. Joint work with . Rahul Santhanam. (Oxford). 2. Minor algorithmic improvements imply lower bounds (Williams, 2010).. NEXP. relaxations. via statistical query complexity. Based on:. V. F.. , Will Perkins, Santosh . Vempala. . . On the Complexity of Random Satisfiability Problems with Planted . Solutions.. STOC 2015. V. F.. Applications. Lecture 5. : Sparse optimization. Zhu Han. University of Houston. Thanks Dr. . Shaohua. Qin’s efforts on slides. 1. Outline (chapter 4). Sparse optimization models. Classic solvers and omitted solvers (BSUM and ADMM). Classification of algorithms. The DIRECT algorithm. Divided rectangles. Exploration and Exploitation as bi-objective optimization. Application to High Speed Civil Transport. Global optimization issues. and Applications. David Crandall, Geoffrey Fox. Indiana University Bloomington. SPIDAL Video Presentation. April 7 2017 . Both Pathology/Remote sensing working on 2D moving to 3D images. Each pathology image could have 10 billion pixels, and we may extract a million spatial objects per image and 100 million features (dozens to 100 features per object) per image. We often tile the image into 4K x 4K tiles for processing. We develop buffering-based tiling to handle boundary-crossing objects. For each typical study, we may have hundreds to thousands of pathology images. 10 Bat Algorithms Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier, 2014 The bat algorithm (BA) is a bio-inspired algorithm developed by Xin-She Yang in 2010. 10.1 Echolocation of Bats Ryan Williams . IBM . Almaden. TexPoint. fonts used in EMF. . Read the . TexPoint. manual before you delete this box.: . A. A. A. A. A. A. A. A. A. A. MOD6. MOD6. The Circuit Class . ACC. . An ACC circuit family .