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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.

169,051 papers · 148 categories

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21426384 · Jun 202019922001200920172026
48 results for non-smooth curves

Study weak Frenet frame for non-smooth curves with finite curvature and torsion.

problem Defining weak binormal and normal for non-smooth curves with finite total curvature and torsion.
method Piecewise linear methods and density argument applied to polygonal curves.
result Weak binormal and normal are rectifiable curves agreeing with total absolute torsion and vector product of tangent indicatrix and weak binormal.

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.

Metrics are semipositively curved if they meet a specific asymptotic condition.

problem Characterizing semipositively curved metrics in Hermitian geometry.
method Proving metrics are semipositively curved if and only if they satisfy an asymptotic extension property.
result Proves a specific condition for Griffiths semipositively curved metrics.

In this paper we discuss a general framework based on symplectic geometry for the study of second order conditions in constrained variational problems on curves. Using the notion of L-derivatives we construct Jacobi curves, which represent a generalization of Jacobi fields from the classical calculus of variations, but…

2018-10-06abs ↗pdf ↗

Optimizes partial AUC across various FPRs for machine learning models.

problem Lack of scalable algorithms for optimizing partial AUC in a range of FPRs.
method Formulated as a non-smooth DC program, developed an efficient approximated gradient descent method using Moreau envelope smoothing.
result Achieved a complexity of O(1/ε6)O(1/ε^6) for finding nearly εε-critical solutions.

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.

This study examines abnormal geodesics in 2D-Zermelo navigation problems, revealing their role in separating time minimal and maximal curves.

problem The role of abnormal geodesics in planar Zermelo navigation problems with strong current.
method Geometric time optimal control approach, focusing on the heading angle of the ship.
result Abnormal geodesics separate time minimal and maximal curves, and are both small-time minimizing and maximizing.

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 ↗

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.

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.

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.

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 ↗

Improved non-smooth optimization methods achieve faster convergence rates.

problem Non-smooth optimization problems, especially in \ell_\infty and 1\ell_1-SVM.
method Higher-order accelerated methods, leveraging recent advances in smooth convex optimization.
result Achieved O(ε4/5)O(ε^{-4/5}) iteration complexity for \ell_\infty regression, breaking previous barriers.

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.

A new shape space allows optimization of non-smooth shapes in fluid mechanics.

problem Optimizing non-smooth shapes in fluid mechanics.
method Constructing a product manifold to include piecewise-smooth shapes.
result Numerical results show applicability in minimizing viscous energy dissipation.

Study proves solenoidal injectivity for tensor fields on curved manifolds with low regularity.

problem Injectivity for tensor fields on negatively curved manifolds with low regularity metrics.
method Pestov energy estimates for transport equation on non-smooth unit sphere bundle, keeping track of regularity, and using functions with more vertical than horizontal regularity.
result Proves solenoidal injectivity for tensor fields on simple Riemannian manifolds with C1,1C^{1,1} metrics and non-positive sectional curvature.

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.

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.

The study analyzes convergence rates for sparse pivotal estimators in high-dimensional regression.

problem Sparse pivotal estimation in high-dimensional regression problems.
method Theoretical analysis and comparison of non-smooth + non-smooth optimization problems, including smoothing techniques.
result Minimax sup-norm convergence rates for square-root Lasso-type estimators are derived.

An extrinsic representation of a Ricci flow on a differentiable n-manifold M is a family of submanifolds S(t), each smoothly embedded in R^{n+k}, evolving as a function of time t such that the metrics induced on the submanifolds S(t) by the ambient Euclidean metric yield the Ricci flow on M. When does such a representa…

2013-11-01abs ↗pdf ↗

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.

Optimizes deep learning pipelines with novel algorithms for smooth and non-smooth functions.

problem Optimizing deep learning pipelines for smooth and non-smooth functions.
method Provided matching lower and upper bounds for smooth convex and non-convex functions, and developed PPRS for non-smooth convex functions.
result PPRS achieves near-linear speed-up and convergence time for non-smooth non-convex problems.

Stochastic approximation proves asymptotic normality for non-smooth problems.

problem Solving non-smooth stochastic approximation problems.
method Stochastic approximation algorithms for solving smooth equations, extended to non-smooth problems.
result Asymptotic normality and optimality in non-smooth stochastic approximation is proven.

Safe-EF improves federated learning for non-smooth, constrained optimization.

problem Federated learning's communication bottlenecks with high-dimensional model updates.
method Error feedback (EF) for non-smooth convex optimization with safety constraints.
result Safe-EF matches lower complexity bounds and ensures safety constraints.

We theoretically discuss why deep neural networks (DNNs) performs better than other models in some cases by investigating statistical properties of DNNs for non-smooth functions. While DNNs have empirically shown higher performance than other standard methods, understanding its mechanism is still a challenging problem.…

2018-02-13abs ↗pdf ↗