Towards Efficient Higher-Order Semidefinite Relaxations for Max-Cut

Optimisation problems involving polynomials arise in a wide variety of contexts, including operational research, statistics, probability, finance, computer science, structural engineering, statistical physics, combinatorial chemistry, computational biology and algorithmic graph theory. They are however extremely challenging to solve, both in theory and practice. Existing algorithms and software are capable of solving only very small instances to proven optimality, unless they have some amenable structure, such as sparsity or convexity.

Read more at: http://www.newton.ac.uk/programmes/POP/

Optimisation problems involving polynomials arise in a wide variety of contexts, including operational research, statistics, probability, finance, computer science, structural engineering, statistical physics, combinatorial chemistry, computational biology and algorithmic graph theory. They are however extremely challenging to solve, both in theory and practice. Existing algorithms and software are capable of solving only very small instances to proven optimality, unless they have some amenable structure, such as sparsity or convexity. Read more at: http://www.newton.ac.uk/programmes/POP/

Share Towards Efficient Higher-Order Semidefinite Relaxations for Max-Cut

Sign up to save your podcasts

Towards Efficient Higher-Order Semidefinite Relaxations for Max-Cut

Towards Efficient Higher-Order Semidefinite Relaxations for Max-Cut

More shows like Polynomial Optimisation

Public Lectures from the Faculty of Law, University of Cambridge

LCIL International Law Centre Podcast

Philosophy

The Stanton Lectures 2012-13

Cambridge Judge Business School Discussions on Health Management

Random Geometry

Faculty of Classics

Medieval History Seminars

Things Seminar

Cambridge Language Sciences