DeepAI AI Chat
Log In Sign Up

Rounding semidefinite programs for large-domain problems via Brownian motion

by   Kevin L. Chang, et al.
Grenoble Institute of Technology
Max Planck Society

We present a new simple method for rounding a semidefinite programming relaxation of a constraint satisfaction problem. We apply it to the problem of approximate angular synchronization. Specifically, we are given directed distances on a circle (i.e., directed angles) between pairs of elements and our goal is to assign the elements to positions on a circle so as to preserve these distances as much as possible. The feasibility of our rounding scheme is based on properties of the well-known stochastic process called Brownian motion. Based on computational and other evidence, we conjecture that this rounding scheme yields an approximation guarantee that is very close to the best-possible guarantee (assuming the Unique-Games Conjecture).


page 1

page 2

page 3

page 4


Approximating Constraint Satisfaction Problems Symmetrically

This thesis investigates the extent to which the optimal value of a cons...

Complex Semidefinite Programming and Max-k-Cut

In a second seminal paper on the application of semidefinite programming...

On recovery guarantees for angular synchronization

The angular synchronization problem of estimating a set of unknown angle...

Sticky Brownian Rounding and its Applications to Constraint Satisfaction Problems

Semidefinite programming is a powerful tool in the design and analysis o...

A Fast Semidefinite Approach to Solving Binary Quadratic Problems

Many computer vision problems can be formulated as binary quadratic prog...

Phototactic Supersmarticles

Smarticles, or smart active particles, are small robots equipped with on...