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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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122244366488 · Jun 202019922001200920172026
48 results for non-smooth spaces

The paper studies moduli spaces of non-smooth metric structures with non-negative Ricci curvature.

problem Understanding moduli spaces of non-smooth metric structures with non-negative Ricci curvature.
method Relating convergence of RCD(0,N)-structures to their lifts, constructing Albanese and soul maps, proving their continuity, and constructing examples.
result Construction of moduli spaces with non-trivial rational homotopy groups.

Smoothness analysis of adversarial training reveals LL_\infty constraints cause more non-smoothness.

problem Non-smoothness of adversarial training loss function.
method Analyzed the smoothness of adversarial training loss function using optimal attacks for model parameters.
result The LL_\infty constraint causes more non-smoothness than L2L_2 constraint.

Timelike curvature and Brunn-Minkowski inequality linked in non-smooth spacetimes.

problem Equivalence between timelike Ricci curvature and Brunn-Minkowski inequality in synthetic Lorentzian spaces.
method Introducing strong qq-timelike Brunn-Minkowski condition and proving equivalence to curvature conditions.
result Timelike curvature dimension condition equivalent to timelike Brunn-Minkowski inequality in specific settings.

Study solves Gel'fand's inverse problem in non-smooth spaces with Ricci curvature bounds.

problem Determining a Riemannian manifold from heat kernel on subsets.
method Analyzes mRCD(K,N){ m RCD}(K,N) spaces with synthetic Ricci curvature bounds.
result Unique solvability of Gel'fand's inverse problem for compact mRCD(K,N){ m RCD}(K,N) spaces.

MARINA-P improves non-smooth federated optimization with adaptive stepsizes.

problem Non-smooth federated optimization in machine learning applications.
method Extends EF21-P and MARINA-P to non-smooth convex setting, proving optimal convergence rate and communication complexity bounds.
result MARINA-P achieves O(1/T)O(1/\sqrt{T}) convergence rate and communication complexity matching classical subgradient methods.

The paper develops bounds and regularity for minimal boundaries in non-smooth spaces with Ricci curvature.

problem Minimal boundaries in non-smooth spaces with Ricci curvature.
method Intrinsic theory of Laplacian bounds, PDE principle, sharp Laplacian bounds on distance function, regularity theory for perimeter-minimizing boundaries.
result Sharp Laplacian bounds and regularity results for perimeter-minimizing boundaries.

We introduce non-smooth symplectic forms on manifolds and describe corresponding Poisson structures on the algebra of Colombeau generalized functions. This is achieved by establishing an extension of the classical map of smooth functions to Hamiltonian vector fields to the setting of non-smooth geometry. For mildly sin…

2014-03-02abs ↗pdf ↗

Study semicontinuity of capacity in non-smooth spaces using intrinsic flat convergence.

problem Investigate semicontinuity of capacity in non-smooth spaces.
method Analyze sequences of local integral current spaces converging in the pointed Sormani-Wenger intrinsic flat sense.
result Prove upper semicontinuity of capacity for balls and Lipschitz sublevel sets under volume-preserving convergence.

We study the dynamics of a particle in a space that is non-differentiable. Non-smooth geometrical objects have an inherently probabilistic nature and, consequently, introduce stochasticity in the motion of a body that lives in their realm. We use the mathematical concept of fiber bundle to characterize the multivalued …

2020-02-04abs ↗pdf ↗

The paper explores various stationarity concepts in non-smooth optimization.

problem Understanding stationarity in non-smooth optimization problems.
method Introduction and discussion of different stationarity concepts for non-convex non-smooth functions.
result Clarification of the relationship among different stationarity concepts and their relevance in iterative methods.

New bounds explain deterministic non-smooth deep nets without large Lipschitz constants.

problem Challenges in explaining generalization of deterministic non-smooth deep nets.
method De-randomized PAC-Bayes margin bounds for deterministic non-convex and non-smooth predictors.
result New bounds avoid large Lipschitz constants, providing generalization guarantees.

In the framework of Lorentzian warped products, we study the Friedmann-Robertson-Walker cosmological model to investigate non-smooth curvatures associated with multiple discontinuities involved in the evolution of the universe. In particular we analyze non-smooth features of the spatially flat Friedmann-Robertson-Walke…

2003-08-16abs ↗pdf ↗

Study generalizes Möbius energy to non-smooth sets in arbitrary dimensions.

problem Investigate Möbius-invariant energies on non-smooth subsets of arbitrary dimensions.
method Show local finite energy implies embedded Lipschitz submanifold, and low fractional Sobolev regularity guarantees finite energy.
result Local graph structure of low fractional Sobolev regularity on a set is sufficient to guarantee finite energy.

This work speeds up hyperparameter selection for non-smooth convex models using implicit differentiation.

problem Optimizing hyperparameters of non-smooth convex models.
method Implicit differentiation of proximal gradient and coordinate descent methods.
result Implicit differentiation can speed up hyperparameter optimization, especially for non-smooth problems.

Develops wavelet-based neural network approximation theory.

problem Analyzing neural network approximation capabilities over various activation functions.
method Wavelet frame theory on spaces of homogeneous type, sufficient conditions for approximation, error estimates.
result Derives sufficient conditions for neural networks to approximate any functions in a given space, including non-smooth activations.

Positive mass theorem for non-smooth metrics on flat manifolds with corners.

problem Proving a positive mass theorem for non-smooth metrics on asymptotically flat manifolds with non-compact boundary.
method Proves a positive mass theorem for metrics that are only continuous across a compact hypersurface.
result Obtains a positive mass theorem on manifolds with non-compact corners.

The paper proposes a method to model non-smooth functions using clustering, classification, and Gaussian process modeling.

problem Modeling discontinuities and non-smoothness in expensive computational models.
method Three-stage approach combining clustering, classification, and Gaussian process modeling.
result The approach successfully models discontinuities and non-smoothness in various functions.

Advances smooth over-parameterization for solving non-smooth optimization problems.

problem Non-smooth optimization with structural constraints in imaging and machine learning.
method Smooth over-parameterization of non-smooth problems, using gradient descent and mirror descent.
result Gradient descent on the reformulated smooth problem converges efficiently without parameter tuning.

New methods improve convergence in non-convex non-smooth learning problems.

problem Sparse learning from high-dimensional data with non-convex, non-smooth regularizers.
method Stochastic proximal gradient methods with arbitrary sampling.
result Independent sampling improves performance over uniform sampling.

Researchers relax the CVF's smoothness requirement to create more flexible flow models.

problem Challenges in constructing flexible density models due to the CVF's smoothness requirement.
method Introduce L\mathcal{L}-diffeomorphisms as generalized transformations that may violate smoothness on zero Lebesgue-measure sets.
result The relaxation allows for the use of non-smooth activation functions like ReLU in residual flows.

New SPS variant improves non-smooth optimization without small gradients.

problem Improving non-smooth optimization without small gradients.
method Safeguarded Stochastic Polyak Step Size (SPSsafe_{safe}) for non-smooth optimization.
result Rigorous convergence guarantees for non-smooth convex optimization without strong assumptions.