Chordal and factor-width decomposition methods for semidefinite programming and
polynomial optimization have recently enabled the analysis and control of large-scale linear …

Recent successes in producing intermediate-scale quantum devices have focused interest
on establishing whether near-term devices could outperform classical computers for …

Convex relaxations have emerged as a promising approach for verifying properties of neural
networks, but widely used using Linear Programming (LP) relaxations only provide …

There is an increasing interest in quantum algorithms for problems of integer programming
and combinatorial optimization. Classical solvers for such problems employ relaxations …

Stochastic hybrid systems have received significant attentions as a relevant modeling
framework describing many systems, from engineering to the life sciences: they enable the …

This work establishes new convergence guarantees for gradient descent in smooth convex
optimization via a computer-assisted analysis technique. Our theory allows nonconstant …

Electric vehicles (EVs) are being rapidly adopted due to their economic and societal
benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend …

This work proposes a new moment-SOS hierarchy, called CS-TSSOS, for solving large-
scale sparse polynomial optimization problems. Its novelty is to exploit simultaneously …

This paper introduces a new robust interior point method analysis for semidefinite
programming (SDP). This new robust analysis can be combined with either logarithmic …

The spectral bundle method developed by Helmberg and Rendl is well-established for
solving large-scale semidefinite programs (SDPs) in the dual form, especially when the …