scholarly journals Hyperbolicity cones and imaginary projections

2018 ◽  
Vol 146 (10) ◽  
pp. 4105-4116 ◽  
Author(s):  
Thorsten Jörgens ◽  
Thorsten Theobald
Keyword(s):  
Hyperbolicity Cones

scholarly journals On hyperbolicity cones associated with elementary symmetric polynomials

2007 ◽  
Vol 2 (3) ◽  
pp. 389-402 ◽  
Author(s):  
Yuriy Zinchenko
Keyword(s):  
Symmetric Polynomials ◽  
Elementary Symmetric Polynomials ◽  
Hyperbolicity Cones

Smooth hyperbolicity cones are spectrahedral shadows

2014 ◽  
Vol 153 (1) ◽  
pp. 213-221 ◽  
Author(s):  
Tim Netzer ◽  
Raman Sanyal
Keyword(s):  
Hyperbolicity Cones

scholarly journals Hyperbolicity cones of elementary symmetric polynomials are spectrahedral

2013 ◽  
Vol 8 (5) ◽  
pp. 1773-1782 ◽  
Author(s):  
Petter Brändén
Keyword(s):  
Symmetric Polynomials ◽  
Elementary Symmetric Polynomials ◽  
Hyperbolicity Cones

scholarly journals On the connectivity of the hyperbolicity region of irreducible polynomials

2019 ◽  
Vol 19 (2) ◽  
pp. 231-233
Author(s):  
Mario Denis Kummer
Keyword(s):  
Hyperbolic Polynomial ◽  
Irreducible Polynomials ◽  
Hyperbolicity Region ◽  
Hyperbolicity Cones

Abstract We give a proof for the fact that an irreducible hyperbolic polynomial has only one pair of hyperbolicity cones. Apart from the use of Bertini’s Theorem the proof is elementary.

scholarly journals Alfonso: Matlab Package for Nonsymmetric Conic Optimization

2021 ◽  
Author(s):  
Dávid Papp ◽  
Sercan Yıldız
Keyword(s):  
Barrier Function ◽  
Optimization Problems ◽  
Symmetric Cone ◽  
Conic Optimization ◽  
Iteration Complexity ◽  
Worst Case ◽  
Optimization Software ◽  
Second Order Cone ◽  
Matlab Package ◽  
Hyperbolicity Cones

We present alfonso, an open-source Matlab package for solving conic optimization problems over nonsymmetric convex cones. The implementation is based on the authors’ corrected analysis of a method of Skajaa and Ye. It enables optimization over any convex cone as long as a logarithmically homogeneous self-concordant barrier is available for the cone or its dual. This includes many nonsymmetric cones, for example, hyperbolicity cones and their duals (such as sum-of-squares cones), semidefinite and second-order cone representable cones, power cones, and the exponential cone. Besides enabling the solution of problems that cannot be cast as optimization problems over a symmetric cone, algorithms for nonsymmetric conic optimization also offer performance advantages for problems whose symmetric cone programming representation requires a large number of auxiliary variables or has a special structure that can be exploited in the barrier computation. The worst-case iteration complexity of alfonso is the best known for nonsymmetric cone optimization: [Formula: see text] iterations to reach an ε-optimal solution, where ν is the barrier parameter of the barrier function used in the optimization. Alfonso can be interfaced with a Matlab function (supplied by the user) that computes the Hessian of a barrier function for the cone. A simplified interface is also available to optimize over the direct product of cones for which a barrier function has already been built into the software. This interface can be easily extended to include new cones. Both interfaces are illustrated by solving linear programs. The oracle interface and the efficiency of alfonso are also demonstrated using an optimal design of experiments problem in which the tailored barrier computation greatly decreases the solution time compared with using state-of-the-art, off-the-shelf conic optimization software. Summary of Contribution: The paper describes an open-source Matlab package for optimization over nonsymmetric cones. A particularly important feature of this software is that, unlike other conic optimization software, it enables optimization over any convex cone as long as a suitable barrier function is available for the cone or its dual, not limiting the user to a small number of specific cones. Nonsymmetric cones for which such barriers are already known include, for example, hyperbolicity cones and their duals (such as sum-of-squares cones), semidefinite and second-order cone representable cones, power cones, and the exponential cone. Thus, the scope of this software is far larger than most current conic optimization software. This does not come at the price of efficiency, as the worst-case iteration complexity of our algorithm matches the iteration complexity of the most successful interior-point methods for symmetric cones. Besides enabling the solution of problems that cannot be cast as optimization problems over a symmetric cone, our software can also offer performance advantages for problems whose symmetric cone programming representation requires a large number of auxiliary variables or has a special structure that can be exploited in the barrier computation. This is also demonstrated in this paper via an example in which our code significantly outperforms Mosek 9 and SCS 2.

scholarly journals Spectrahedrality of hyperbolicity cones of multivariate matching polynomials

2018 ◽  
Vol 50 (2) ◽  
pp. 165-190 ◽  
Author(s):  
Nima Amini
Keyword(s):  
Hyperbolicity Cones

scholarly journals Exponential Lower Bounds on Spectrahedral Representations of Hyperbolicity Cones

2019 ◽  
pp. 2322-2332
Author(s):  
Prasad Raghavendra ◽  
Nick Ryder ◽  
Nikhil Srivastava ◽  
Benjamin Weitz
Keyword(s):  
Lower Bounds ◽  
Hyperbolicity Cones
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