Professors Amir Ali Ahmadi and Pravesh Kothari are pioneering the use of artificial intelligence to enhance complex mathematical proof systems, with applications in robotics, optimization, and ...

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Reliable trajectory-based nonadiabatic quantum dynamics methods at the atomic/molecular level are critical for the practical understanding and rational design of many important processes in real large ...

Describe your issue. This bug report demonstrates inconsistent behavior (see Case 1 and Case 2 below) in SciPy's eigenvalue calculations for singular positive semidefinite (PSD) correlation matrice ...

We develop and implement efficient semidefinite program- ming algorithms to rigorously and numerically determine the energy spectra of confining polynomial potentials in one dimension.

In this paper, we explore the application of semidefinite programming to the realm of quantum codes, specifically focusing on codeword stabilized (CWS) codes with entanglement assistance. Notably, we ...

E6602 is typically taught once per year in the Spring semester. The information below is meant to provide a snapshot of the material covered. Students will learn to recognize, model, formulate and ...

In practice, semidefinite programs are notoriously slow, and so speedups are welcome. In this paper, we show how to sketch a popular semidefinite relaxation of a graph clustering problem known as ...

July 2015: Talk at the Modeling and Optimization: Theory and Applications (MOPTA) on A Fast Distributed Algorithm for Sparse Semidefinite Programs. July 2015: Talk at the International Symposium on ...

A hierarchy of semidefinite programming (SDP) relaxations approximates the global optimum of polynomial optimization problems of noncommuting variables. Generating the relaxation, however, is a ...