3 papers
math.OC2022
Escaping From Saddle Points Using Asynchronous Coordinate Gradient Descent
Marco Bornstein, Jin-Peng Liu, Jingling Li +1
Large-scale non-convex optimization problems are expensive to solve due to computational and memory costs. To reduce the costs, first-order (computationally efficient) and asynchro…
quant-ph2020
Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance
Dong An, Noah Linden, Jin-Peng Liu +3
Inspired by recent progress in quantum algorithms for ordinary and partial differential equations, we study quantum algorithms for stochastic differential equations (SDEs). Firstly…
quant-ph2020
Solving generalized eigenvalue problems by ordinary differential equations on a quantum computer
Changpeng Shao, Jin-Peng Liu
Many eigenvalue problems arising in practice are often of the generalized form $A\x=λB\x$. One particularly important case is symmetric, namely are Hermitian and is posi…