Solving SDD linear systems in time Õ(mlog nlog(1/ε))

نویسندگان

  • Ioannis Koutis
  • Gary L. Miller
  • Richard Peng
چکیده

We present an algorithm that on input of an n×n symmetric diagonally dominant matrix A with m non-zero entries constructs in time Õ(m log n) a solver which on input of a vector b computes a vector x satisfying ||x−Ab||A < �||Ab||A in time Õ(m log n log(1/�)) 1. The new algorithm exploits previously unknown structural properties of the output of the incremental sparsification algorithm given in [Koutis,Miller,Peng, FOCS 2010]. We also accelerate the construction of low-stretch spanning trees by rounding the edge weights to ensure that each iteration of the hierarchical star decomposition encounters a small number of distinct edge lengths.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Near Linear-Work Parallel SDD Solvers, Low-Diameter Decomposition, and Low-Stretch Subgraphs

This paper presents the design and analysis of a near linear-work parallel algorithm for solving symmetric diagonally dominant (SDD) linear systems. On input of a SDD n-by-n matrix A with m nonzero entries and a vector b, our algorithm computes a vector x̃ such that ‖x̃−Ab‖A ≤ ε · ‖Ab‖A in O(m log n log 1ε ) work and O(m log 1 ε ) depth for any fixed θ > 0. The algorithm relies on a parallel algo...

متن کامل

0 Approaching optimality for solving SDD linear systems ∗

We present an algorithm that on input a graph G with n vertices and m+ n− 1 edges and a value k, produces an incremental sparsifier Ĝ with n − 1 +m/k edges, such that the condition number of G with Ĝ is bounded above by Õ(k log n), with probability 1− p. The algorithm runs in time Õ((m log n+ n log n) log(1/p)). As a result, we obtain an algorithm that on input an n × n symmetric diagonally dom...

متن کامل

A ug 2 01 0 Approaching optimality for solving SDD linear systems ∗

We present an algorithm that on input of an n-vertex m-edge weighted graph G and a value k, produces an incremental sparsifier Ĝ with n− 1+m/k edges, such that the condition number of G with Ĝ is bounded above by Õ(k log n), with probability 1− p. The algorithm runs in time Õ((m logn+ n log n) log(1/p)). As a result, we obtain an algorithm that on input of an n × n symmetric diagonally dominant...

متن کامل

Improved Spectral Sparsification and Numerical Algorithms for SDD Matrices

We present three spectral sparsification algorithms that, on input a graph G with n vertices and m edges, return a graph H with n vertices and O(n logn/ 2) edges that provides a strong approximation of G. Namely, for all vectors x and any > 0, we have (1− )xLGx ≤ xLHx ≤ (1 + )xLGx, where LG and LH are the Laplacians of the two graphs. The first algorithm is a simple modification of the fastest ...

متن کامل

Path Finding I :Solving Linear Programs with \~O(sqrt(rank)) Linear System Solves

In this paper we present a new algorithm for solving linear programs that requires only Õ( √ rank(A)L) iterations where A is the constraint matrix of a linear program with m constraints and n variables and L is the bit complexity of a linear program. Each iteration of our method consists of solving Õ(1) linear systems and additional nearly linear time computation. Our method improves upon the p...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • CoRR

دوره abs/1102.4842  شماره 

صفحات  -

تاریخ انتشار 2011