5 accepted papers
Compressed sensing, which involves reconstruction of sparse signals from an under-determined linear system, has been recently applied to problems in group testing to save on the number of tests administered during a pandemic or other resource-constrained scenarios. In practical group testing in time…
The difference of convex (DC) algorithm is a conceptually simple method for the minimization of (non)convex functions that are expressed as the difference of two convex functions. An attractive feature of the algorithm is that it maintains a global overestimator on the function and does not require…
We consider the problem of solving large-scale instances of the Max-Cut semidefinite program (SDP), i.e., optimizing a linear function over $n\times n$ positive semidefinite (PSD) matrices with unit diagonal. When the cost matrix is PSD, we show how to exactly reformulate the problem as maximizing a…
Max-k-Cut and correlation clustering are fundamental graph partitioning problems. For a graph $G=(V,E)$ with $n$ vertices, the methods with the best approximation guarantees for Max-k-Cut and the Max-Agree variant of correlation clustering involve solving SDPs with $\mathcal{O}(n^2)$ constraints and…
Phaseless super-resolution is the problem of reconstructing a signal from its low-frequency Fourier magnitude measurements. It is the combination of two classic signal processing problems: phase retrieval and super-resolution. Due to the absence of phase and high-frequency measurements, additional i…