RSOM applies dictionary learning to discover a sparse dictionary that conditions the analytic continuation inverse problem, yielding competitive results on synthetic tests and finite-temperature electron gas QMC data.
Trefethen.Approximation Theory and Approximation Practice
7 Pith papers cite this work. Polarity classification is still indexing.
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Quantum algorithm block-encodes Riccati solutions for m-particle m-hole RPA using Riesz projectors and QSVT, claiming linear system-size scaling under sparsity and polynomial cost in excitation rank m.
Chebyshev polynomial approximations with adaptive sampling solve canonical differential equations for Feynman integrals, demonstrated to be stable and competitive for two-loop five-point cases in double precision.
A general framework for iterative contour integral-based methods for nonlinear eigenvalue problems is introduced, enabling a proof of linear convergence for NLFEAST under mild assumptions.
Introduces formal verification to compute certified neuron range bounds for CKKS-encrypted neural networks, eliminating overflow failures that previously reached 47%.
CHESS package implements Chebyshev-Lobatto spectral collocation for transporting epsilon-factorized differential equations of Feynman master integrals with benchmarks showing rapid convergence and shorter wall times than local series methods.
Case study applying Chebfun's Chebyshev-based methods to Richards' equation shows explicit linearization and L-scheme overcome Newton convergence failures of chebop for accurate 1D steady-state solutions.
citing papers explorer
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Discovering a well-conditioned analytic continuation problem via dictionary learning
RSOM applies dictionary learning to discover a sparse dictionary that conditions the analytic continuation inverse problem, yielding competitive results on synthetic tests and finite-temperature electron gas QMC data.
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Quantum Solvers for Nonlinear Matrix Equations in Quantum Chemistry
Quantum algorithm block-encodes Riccati solutions for m-particle m-hole RPA using Riesz projectors and QSVT, claiming linear system-size scaling under sparsity and polynomial cost in excitation rank m.
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Chebyshev Approximations of Feynman Integrals for Collider Physics
Chebyshev polynomial approximations with adaptive sampling solve canonical differential equations for Feynman integrals, demonstrated to be stable and competitive for two-loop five-point cases in double precision.
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Linear convergence of iterative contour integral-based eigensolvers for nonlinear eigenvalue problems
A general framework for iterative contour integral-based methods for nonlinear eigenvalue problems is introduced, enabling a proof of linear convergence for NLFEAST under mild assumptions.
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Encrypted Neural Networks without Overflows
Introduces formal verification to compute certified neuron range bounds for CKKS-encrypted neural networks, eliminating overflow failures that previously reached 47%.
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CHESS: CHEbyshev pSeudo-Spectral transport for Feynman integral differential equations
CHESS package implements Chebyshev-Lobatto spectral collocation for transporting epsilon-factorized differential equations of Feynman master integrals with benchmarks showing rapid convergence and shorter wall times than local series methods.
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Programming with Chebfun. Case study: Richards equation
Case study applying Chebfun's Chebyshev-based methods to Richards' equation shows explicit linearization and L-scheme overcome Newton convergence failures of chebop for accurate 1D steady-state solutions.