754 research outputs found
A unified variance-reduced accelerated gradient method for convex optimization
We propose a novel randomized incremental gradient algorithm, namely,
VAriance-Reduced Accelerated Gradient (Varag), for finite-sum optimization.
Equipped with a unified step-size policy that adjusts itself to the value of
the condition number, Varag exhibits the unified optimal rates of convergence
for solving smooth convex finite-sum problems directly regardless of their
strong convexity. Moreover, Varag is the first accelerated randomized
incremental gradient method that benefits from the strong convexity of the
data-fidelity term to achieve the optimal linear convergence. It also
establishes an optimal linear rate of convergence for solving a wide class of
problems only satisfying a certain error bound condition rather than strong
convexity. Varag can also be extended to solve stochastic finite-sum problems.Comment: 33rd Conference on Neural Information Processing Systems (NeurIPS
2019
Light sterile neutrinos and lepton-number-violating kaon decays in effective field theory
We investigate lepton-number-violating decays in the presence of sterile neutrinos. We consider minimal
interactions with Standard-Model fields through Yukawa couplings as well as
higher-dimensional operators in the framework of the neutrino-extended Standard
Model Effective Field Theory. We use chiral perturbation theory to
match to mesonic interactions and compute the lepton-number-violating decay
rate in terms of the neutrino masses and the Wilson coefficients of
higher-dimensional operators. For neutrinos that can be produced on-shell, the
decay rates are highly enhanced and higher-dimensional interactions can be
probed up to very high scales around (30) TeV.Comment: 31 pages, 6 figure
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