In this paper, we establish various L2-estimates for the exterior differential operator on p-convex Riemannian manifolds in the sense of Harvey and Lawson. As geometric applications, we prove vanishing and finiteness results for the de Rham cohomology groups.
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7 results for “L2-estimate”
New characterizations of curvature operators for specific forms via L2-estimates.
problem Characterizing semi-positive and semi-negative curvature operators for and -forms.
method Using L2-estimates to characterize curvature operators for and -forms.
result New characterizations of Nakano semi-positivity and semi-negativity.
The purpose of this paper is to establish an injectivity theorem generalized to pseudo-effective line bundles with transcendental (non-algebraic) singular hermitian metrics and multiplier ideal sheaves. As an application, we obtain a Nadel type vanishing theorem. For the proof, we study the asymptotic behavior of the h…
New bounds for Lasso and Group Lasso in high dimensions derived.
problem Estimation error bounds for Lasso and Group Lasso in high-dimensional settings.
method Recent advances in high-dimensional statistics to derive new L2 estimation upper bounds.
result Bounds match optimal minimax rate for Lasso and improve over existing results for Group Lasso.
Some Kollar-Enoki type injectivity and Nadel type vanishing theorems on compact Kahler manifoldsmath.AG
The paper proves injectivity and vanishing theorems on compact Kahler manifolds.
problem Injectivity and vanishing theorems on compact Kahler manifolds.
method Hodge theory, Bochner-Kodaira-Nakano identity, analytic method, transcendental method, Demailly-Peternell-Schneider equisingular approximation theorem, Hormander L2 estimates.
result The main injectivity theorem implies several Nadel type vanishing theorems.
The paper characterizes SLOPE's trade-off between FDP and TPP, showing its power limit and superiority over Lasso.
problem Characterizing the SLOPE trade-off between FDP and TPP.
method Using variational perspective and Gaussian random designs, the paper derives upper and lower bounds on the optimal trade-off.
result SLOPE outperforms Lasso in terms of FDP, TPP, and l2 estimation risk.
We study a family of sparse estimators defined as minimizers of some empirical Lipschitz loss function -- which include the hinge loss, the logistic loss and the quantile regression loss -- with a convex, sparse or group-sparse regularization. In particular, we consider the L1 norm on the coefficients, its sorted Slope…