Optimistic bilevel optimization with manifold lower-level minimizers is differentiable if the optimistic selection is unique, yielding a pseudoinverse hyper-gradient and a convergent HG-MS algorithm whose rate depends on intrinsic manifold dimension.
arXiv preprint arXiv:1501.01571 , year=
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abstract
In recent years, random matrices have come to play a major role in computational mathematics, but most of the classical areas of random matrix theory remain the province of experts. Over the last decade, with the advent of matrix concentration inequalities, research has advanced to the point where we can conquer many (formerly) challenging problems with a page or two of arithmetic. The aim of this monograph is to describe the most successful methods from this area along with some interesting examples that these techniques can illuminate.
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Rigorous worst- and average-case error bounds show comparable worst-case scaling for digital and analog quantum simulators under perturbative noise, with distinct average-case error cancellation and concentration bounds for Gaussian and Brownian noise.
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Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets
Optimistic bilevel optimization with manifold lower-level minimizers is differentiable if the optimistic selection is unique, yielding a pseudoinverse hyper-gradient and a convergent HG-MS algorithm whose rate depends on intrinsic manifold dimension.
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Stability of digital and analog quantum simulations under noise
Rigorous worst- and average-case error bounds show comparable worst-case scaling for digital and analog quantum simulators under perturbative noise, with distinct average-case error cancellation and concentration bounds for Gaussian and Brownian noise.