PPT-Point registration via efficient convex relaxation

Haggai Maron Nadav Dym Itay Kezurer Shahar Kovalsky Yaron Lipman Weizmann Institute of Science 1 Orthogonal         Orthogonal Procrustes Problem. Consider all possible pairs of points in the set and consider the line segment connecting any such pair. All such line segments must lie entirely within the set.. Convex Set of Points. Convex –vs- Nonconvex. 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.. Guo. . Qi, . Chen . Zhenghai. , Wang . Guanhua. , Shen . Shiqi. , . Himeshi. De Silva. Outline. Introduction: Background & Definition of convex . hull. Three . algorithms. Graham’s Scan. Jarvis March. Section 6.2. Learning Goal. We will use our knowledge of the characteristics. of solids so that we can match a convex. polyhedron to its net. We’ll know we’ve got it. when we’re able to create a net for a given solid.. general . submodular. functions. CVPR 2015 . Tutorial. Stefanie Jegelka. MIT. The set function view. 2. cost. of buying items . together, or. utility, . or. probability, …. (. . ). http://. www.robots.ox.ac.uk. /~oval/. Slides available online http://. mpawankumar.info. Convex Sets. Convex Functions. Convex Program. Outline. Convex Set. x. 1. x. 2. λ. . x. 1. (1 - . λ. ) . 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 . machine learning. Yuchen Zhang. Stanford University. Non-convexity . in . modern machine learning. 2. State-of-the-art AI models are learnt by minimizing (often non-convex) loss functions.. T. raditional . 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.. M. Pawan Kumar. pawan.kumar@ecp.fr. Slides available online http://. cvn.ecp.fr. /personnel/. pawan. Recap. V. a. V. b. V. c. d. a. d. b. d. c. Label . l. 0. Label . l. 1. D. : Observed data (image). Partially Based on WORK FROM Microsoft Research With:. 1. 1, 3. 4-->5. 1: MSR Redmond 2: Weizmann Institute 3: University of Washington 4: Stanford 5: CMU. Sébastien Bubeck, Bo’az Klartag, Yin Tat Lee, Yuanzhi Li. Nicholas . Ruozzi. University of Texas at Dallas. Where We’re Going. Multivariable calculus tells us where to look for global optima, but our goal is to design algorithms that can actually find one!. Lecture 2 . Convex Set. CK Cheng. Dept. of Computer Science and Engineering. University of California, San Diego. Convex Optimization Problem:. 2. . is a convex function. For . , .  .  . Subject to. The . state of being free from tension and anxiety. .. For many of us, relaxation means zoning out in front of the TV at the end of a stressful day. But this does little to reduce the damaging effects of stress. To effectively combat stress, we need to activate the body’s natural relaxation response. You can do this by practicing relaxation techniques such as deep breathing, meditation, rhythmic exercise, and yoga. Fitting these activities into your life can help reduce everyday stress, boost your energy and mood, and improve your mental and physical health.