Published:
We integrate Adam and momentume optimizers with the continuous variable Coherent Ising Machine (CV-CIM) dynamical solver. We show that both optimization techniques can improve the convergence speed and sample diversity of the CV-CIM, while Adam improves the stability of the resulting system
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Published in European Control Conference, 2020
We develop a rigorous measure of “locality” that relates the structural properties of a linearly-constrained convex optimization problem to the amount of information that agents should exchange to compute an arbitrarily high-quality approximation of its solution. We leverage the notion of locality to develop a locality-aware distributed optimization algorithm.
Available here
Published in IEEE Transactions on Control of Network Systems, 2021
We extend our prior work by providing tighter bounds on the locality of problems through the conjugate-gradient algorithm, allowing the decay results to be applied to all linearly constrained strongly convex optimization problems.
Available here
Published in Int. Conf. on Artificial Intelligence and Statistics, 2022
We unify several SDP relaxations for ReLU neural network verification by providing an exact convex formulation as a completely-positive program. This provides a path for relaxations that systematically trade off tightness and efficiency.
Available here
Published in SIAM Journal of Optimization, 2023
We present a formal analysis of hybrid algorithms in the context of solving mixed-binary quadratic programs (MBQP) via Ising solvers. We leverage copositive optimization and cutting-plane algorithms to derive an algorithm that provable shifts complexity onto the subroutine handled by the Ising solver.
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Published:
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California Institute of Technology, 2015 - 2018
California Institute of Technology, 2018
California Institute of Technology, 2018
Stanford University, 2020
Stanford University, 2021