2 citations · 2 across the 3 of their papers we have counts for
6 papers
Memory-Efficient Approximation Algorithms for Max-k-Cut and Correlation Clustering
Nimita Shinde, Vishnu Narayanan, James Saunderson
Max-k-Cut and correlation clustering are fundamental graph partitioning problems. For a graph with G=(V,E) with n vertices, the methods with the best approximation guarantees for M…
Certifying polynomial nonnegativity via hyperbolic optimization
James Saunderson
We describe a new approach to certifying the global nonnegativity of multivariate polynomials by solving hyperbolic optimization problems---a class of convex optimization problems…
Limitations on the expressive power of convex cones without long chains of faces
James Saunderson
A convex optimization problem in conic form involves minimizing a linear functional over the intersection of a convex cone and an affine subspace. In some cases, it is possible to…
Estimating the Spectral Density of Large Implicit Matrices
Ryan P. Adams, Jeffrey Pennington, Matthew J. Johnson +4
Many important problems are characterized by the eigenvalues of a large matrix. For example, the difficulty of many optimization problems, such as those arising from the fitting of…
Competitive Online Algorithms for Resource Allocation over the Positive Semidefinite Cone
Reza Eghbali, James Saunderson, Maryam Fazel
We consider a new and general online resource allocation problem, where the goal is to maximize a function of a positive semidefinite (PSD) matrix with a scalar budget constraint.…
Improving Efficiency and Scalability of Sum of Squares Optimization: Recent Advances and Limitations
Amir Ali Ahmadi, Georgina Hall, Antonis Papachristodoulou +2
It is well-known that any sum of squares (SOS) program can be cast as a semidefinite program (SDP) of a particular structure and that therein lies the computational bottleneck for…