PPT-Online Convex Optimization Using Predictions

Niangjun Chen Joint work with Anish Agarwal Lachlan Andrew Siddharth Barman and Adam Wierman 1         2             3                 online   switching cost. 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. Pritam. . Sukumar. & Daphne Tsatsoulis. CS 546: Machine Learning for Natural Language Processing. 1. What is Optimization?. Find the minimum or maximum of an objective function given a set of constraints:. Nonconvex Polynomials with . Algebraic . Techniques. Georgina . Hall. Princeton, ORFE. Joint work with . Amir Ali Ahmadi. Princeton, ORFE. 1. 7/13/2015. MOPTA . 2015. Difference of Convex (DC) programming. Problems in Ramsey theory typically ask a question of the form: "how many elements of some structure must there be to guarantee that a particular property will hold?“. Here we consider geometric Ramsey-type results about finite point sets in the plane.. Lenses. A . convex lens. (or a . converging lens. ) converges parallel light rays passing through it.. Various shapes of convex lenses. Terms for describing lenses. Optical centre. is the centre of a lens.. 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. for Sequential Game Solving. Overview. Sequence-form transformation. Bilinear saddle-point problems. EGT/Mirror . prox. Smoothing techniques for sequential games. Sampling techniques. Some experimental results. 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. ) . Majorization. ANNA . SHTENGEL, Weizmann Institute of Science. ROI PORANNE and OLGA SORKINE-HORNUNG, ETH Zurich. SHAHAR Z. KOVALSKY, Duke University. YARON LIPMAN, Weizmann Institute of . Science. ACM Transactions on Graphics . 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.. Motivation and IntroductionHow to employ data for optimal control? Plant DisturbanceInputController CostsConstraints State •Model-Free RL simultaneously parameterize -Poor data efficiency-Dynamic Date Monday June 17 2013 till Thursday June 20 2013TimeVenue Included 2 Co31ee Breaks and a Lunch EE Short CourseTopics to be CoveredDue to the limited space RSVP is required byemailing the local coo A planar region . . is called . convex. if and only if for any pair . of points . , . in . , the line segment . lies . completely. in . . .  . Otherwise, it is called . concave. . . Convex.  . 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.