Peter Richtarik Operational Research and Optimization Extreme* Mountain Climbing * in a billion dimensional space on a foggy day.

Slides:

Advertisements

Similar presentations

Peter Richtárik Parallel coordinate Simons Institute for the Theory of Computing, Berkeley Parallel and Distributed Algorithms for Inference and Optimization,

Advertisements

Max output (Q) subject to a cost constraint

1 © 2006 by Nelson, a division of Thomson Canada Limited Slides developed by: William Rentz & Al Kahl University of Ottawa Web Appendix 4A Production Decisions.

Slide 1 EE3J2 Data Mining EE3J2 Data Mining Lecture 11: K-Means Clustering Martin Russell.

© 2013 IBM Corporation Complexity must be conquered (Data is more than “just” big) Laura Haas IBM Almaden Research Center 1.

Peter Richtarik Why parallelizing like crazy and being lazy can be good.

Parallel Coordinates Representation of multi-dimensional data Discovery Process xmdv Visualization Tool Ganesh K. Panchanathan Christa M. Chewar.

Peter Richtárik Distributed Coordinate Descent For Big Data Optimization Numerical Algorithms and Intelligent Software - Edinburgh – December 5,

Peter Richtarik School of Mathematics Optimization with Big Data * in a billion dimensional space on a foggy day Extreme* Mountain Climbing =

Peter Richtárik Parallel coordinate NIPS 2013, Lake Tahoe descent methods.

15 March 04 GC2 Systems Thinking Workshop. What is a System? “An interconnected set of elements that is coherently organized around some purpose” -Dana.

Lecture outline Support vector machines. Support Vector Machines Find a linear hyperplane (decision boundary) that will separate the data.

Support Vector Machines

Lecture 10: Support Vector Machines

Pattern Recognition. Introduction. Definitions.. Recognition process. Recognition process relates input signal to the stored concepts about the object.

Path Planning of Robot in Three- dimensional Grid Environment based on Genetic Algorithms Hua Zhang, Manlu Liu, Ran Liu, Tianlian Hu Intelligent Control.

Peter Richtárik (joint work with Martin Takáč) Distributed Coordinate Descent Method AmpLab All Hands Meeting - Berkeley - October 29, 2013.

XBRL Update Arthur West, Companies House, GB 247,527 GB accounts filed as XBRL in

1 Yang Yang, Jie Tang, Juanzi Li Tsinghua University Walter Luyten, Marie-Francine Moens Katholieke Universiteit Leuven Lu Liu Northwestern University.

1 IE 607 Heuristic Optimization Simulated Annealing.

Structure Preserving Embedding Blake Shaw, Tony Jebara ICML 2009 (Best Student Paper nominee) Presented by Feng Chen.

J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 2.

Ken YoussefiMechanical Engineering Dept. 1 Design Optimization Optimization is a component of design process The design of systems can be formulated as.

STDM - Linear Programming 1 By Isuru Manawadu B.Sc in Accounting Sp. (USJP), ACA, AFM

Ken YoussefiMechanical Engineering Dept. 1 Design Optimization Optimization is a component of design process The design of systems can be formulated as.

P I can solve linear programing problem. Finding the minimum or maximum value of some quantity. Linear programming is a form of optimization where.

Machine Learning Using Support Vector Machines (Paper Review) Presented to: Prof. Dr. Mohamed Batouche Prepared By: Asma B. Al-Saleh Amani A. Al-Ajlan.

Simplex Method Adapting to Other Forms.  Until now, we have dealt with the standard form of the Simplex method  What if the model has a non-standard.

Инвестиционный паспорт Муниципального образования «Целинский район»

Richard Kelley Motion Planning on a GPU. Last Time Nvidia’s white paper Productive discussion.

OR Chapter 2. Simplex method (2,0) (2,2/3) (1,2)(0,2)

(x – 8) (x + 8) = 0 x – 8 = 0 x + 8 = x = 8 x = (x + 5) (x + 2) = 0 x + 5 = 0 x + 2 = x = - 5 x = - 2.

Cooperative Classifiers Rozita Dara Supervisor: Prof. Kamel Pattern Analysis and Machine Intelligence Lab University of Waterloo.

Kernel Methods: Support Vector Machines Maximum Margin Classifiers and Support Vector Machines.

Mehdi Ghayoumi MSB rm 132 Ofc hr: Thur, a Machine Learning.

Linear hyperplanes as classifiers Usman Roshan. Hyperplane separators.

University of Texas at Austin Machine Learning Group Department of Computer Sciences University of Texas at Austin Support Vector Machines.

Overview of Emerging IT2 At the request of several students, the context and mathematics of several Big Data algorithms will be examined in detail Dissertation.

Final Exam Review CS479/679 Pattern Recognition Dr. George Bebis 1.

In The Name of God. Parallel processing Course Evaluation  Final Exam is closed book( 14 Scores)  Research and Presentation, Quizzes (5 Scores)  No.

Kernel Methods: Support Vector Machines Maximum Margin Classifiers and Support Vector Machines.

照片档案整理一、照片档案的含义二、照片档案的归档范围三、卷内照片的分类、组卷、排序与编号四、填写照片档案说明五、照片档案编目及封面、备考填写六、数码照片整理方法七、照片档案的保管与保护.

공무원연금관리공단 광주지부 공무원대부등 공적연금 연계제도 공무원연금관리공단 광주지부. 공적연금 연계제도 국민연금과 직역연금 ( 공무원 / 사학 / 군인 / 별정우체국 ) 간의 연계가 이루어지지 않고 있 어 공적연금의 사각지대가 발생해 노후생활안정 달성 미흡 연계제도 시행전.

Жюль Верн ( ). Я мальчиком мечтал, читая Жюля Верна, Что тени вымысла плоть обретут для нас; Что поплывет судно громадней «Грейт Истерна»; Что.

Machine Learning Artificial Neural Networks MPλ ∀ Stergiou Theodoros 1.

A Brief Introduction to Support Vector Machine (SVM) Most slides were from Prof. A. W. Moore, School of Computer Science, Carnegie Mellon University.

Support Vector Machines Reading: Textbook, Chapter 5 Ben-Hur and Weston, A User’s Guide to Support Vector Machines (linked from class web page)

Deep Feedforward Networks

Design and Analysis of ALGORITHM (Session 1)

Distributed Computation Framework for Machine Learning

Analytics and OR DP- summary.

BUPT final year projects

Joelle Pineau: General info

A weight-incorporated similarity-based clustering ensemble method based on swarm intelligence Yue Ming NJIT#:

Topological Ordering Algorithm: Example

CSSE463: Image Recognition Day 14

The Big 6 Research Model Step 3: Location and Access

አሮጌው በር.

Overview of Emerging IT1 & IT2

CSSE463: Image Recognition Day 14

Structural Optimization Design ( Structural Analysis & Optimization )

CSSE463: Image Recognition Day 14

Topological Ordering Algorithm: Example

CSSE463: Image Recognition Day 14

Topological Ordering Algorithm: Example

Topological Ordering Algorithm: Example

Algorithms Lecture # 26 Dr. Sohail Aslam.

THE GROWTH ENVIRONMENT.

Presentation transcript:

Peter Richtarik Operational Research and Optimization Extreme* Mountain Climbing * in a billion dimensional space on a foggy day

Western General Hospital ( Creutzfeldt-Jakob Disease) Arup (Truss Topology Design) Ministry of Defence dstl lab (Algorithms for Data Simplicity) Royal Observatory (Optimal Planet Growth)

GOD’S Algorithm = Teleportation

If you are not a God... x0x0 x1x1 x2x2 x3x3

4-Dial Lock F : {0,1,...,9} 4 R Combination maximizing F opens the lock

BIG DATA

How to Open a Lock with Billion Interconnected Dials? F : R 1,000,000,000 R

Parallel Coordinate Descent

Theory vs Reality

Research Paper

Algorithms

Probability

Inequalities

Complexity

TOOLS Probability Machine LearningMatrix Theory HPC