PPT-V3 Matrix algorithms and

graph partitioning Dividing networks into clusters Graph partitioning The Kernighan Lin algorithm Spectral partitioning 1 SS 2014 lecture 3 Mathematics of Biological Networks. 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 . Lecture 18. The basics of graphs.. 8/25/2009. 1. ALG0183 Algorithms & Data Structures by Dr Andy Brooks. Watch out for self-loops in graphs.. 8/25/2009. ALG0183 Algorithms & Data Structures by Dr Andy Brooks. Elad. . Hazan. (. Technion. ). Satyen Kale . (Yahoo! Labs). Shai. . Shalev-Shwartz. (Hebrew University). Three Prediction Problems: . I. Online Collaborative Filtering. Users: . {1, 2, …, m}. Movies: . Strassen's. Matrix Multiplication . Algorithms. . Sarah M. . Loos. . Undergraduate, Computer Science, Indiana University, smloos@indiana.edu . A very simple recasting of this classic 7-multiplication recursion improves its time performance for rectangular matrices of order . Sparse Matrix-Matrix Multiplication and Its Use in . Triangle Counting and Enumeration. Ariful Azad . Lawrence Berkeley National Laboratory. SIAM ALA 2015, Atlanta. In collaboration with. Grey Ballard (Sandia), . Huang, Ph.D., Professor. Email. ：. yhuang@nju.edu.cn. NJU-PASA Lab for Big Data Processing. Department of Computer Science and Technology. Nanjing University. May 29, 2015, India. A Unified Programming Model . real-weighted APSP. Raphael Yuster. University of Haifa. 2. Permutations and matrix products. This work consists of several algorithms and applications that involve the manipulation of . sets of permutations via matrix multiplication. 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). Richard Peng. Georgia Tech. OUtline. (Structured) Linear Systems. Iterative and Direct Methods. (. Graph) . Sparsification. Sparsified. Squaring. Speeding up Gaussian Elimination. Graph Laplacians. 1. In this topic, we will cover:. Traversals of trees and graphs. Backtracking . Backtracking. Suppose a solution can be made as a result of a series of choices. Each choice forms a partial solution. These choices may form either a tree or DAG. Richard Peng. Georgia Tech. OUtline. (Structured) Linear Systems. Iterative and Direct Methods. (. Graph) . Sparsification. Sparsified. Squaring. Speeding up Gaussian Elimination. Graph Laplacians. 1. John R. Gilbert (. gilbert@cs.ucsb.edu. ). www.cs.ucsb.edu/~gilbert/. cs219. Systems of linear equations:. . Ax = . b. Eigenvalues and eigenvectors:. Aw = . λw. Systems of linear equations: Ax = b. Informatics and Control Systems Faculty. New . tweakable. block cipher. Student: . L. evan. . Julakidze. Informatics and Control Systems Faculty. Doctorate II year. Leader: . Zurab. . K. ochladze. Jim . Demmel. EECS & Math Departments. UC Berkeley. Why avoid communication? . Communication = moving data. Between level of memory hierarchy. Between processors over a network. Running time of an algorithm is sum of 3 terms:.