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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.

168,657 papers · 148 categories

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48 results for first-order

Study first-order locally convex Lie algebroids in Bastiani calculus.

problem Define and study first-order locally convex Lie algebroids.
method Define sheaves of Lie algebroid forms and morphisms, prove category structure, study representations and cohomology.
result First-order locally convex Lie algebroids form a category and have applications in Lie II theorems.

Extends a theorem for first-order elliptic operators on manifolds.

problem Proving the relative index theorem for general first-order elliptic operators.
method Using boundary value problems and graphical decomposition of elliptically regular boundary conditions.
result Proves the relative index theorem for general first-order elliptic operators.

We consider first order expansions of convex penalized estimators in high-dimensional regression problems with random designs. Our setting includes linear regression and logistic regression as special cases. For a given penalty function hh and the corresponding penalized estimator β^\hatβ, we construct a quantity ηη,…

2019-10-12abs ↗pdf ↗

Optimal first-order methods are shown to be fundamental limits in functional estimation.

problem Optimal functional estimation under weak conditions.
method Formalization of functional estimation with black-box nuisance function estimates and derivation of minimax lower bounds.
result First-order methods are optimal under weak conditions, but higher-order methods can outperform them when nuisance function structure is known.

First order discretizations of Langevin diffusion can achieve better generalization error with additional smoothness assumptions.

problem Analyzing generalization error for first order discretizations of Langevin diffusion.
method Providing a sufficient smoothness condition to show that first order methods can achieve arbitrarily runtime complexity for a given expected generalization error.
result First order methods can achieve arbitrarily runtime complexity with additional smoothness assumptions.

The aim of this paper is fourfold. Firstly, we introduce and study the f-ultra-harmonic maps. Secondly, we recall the geometric dynamics generated by a first order normal PDE system and we give original results regarding the geometric dynamics generated by other first order PDE systems. Thirdly, we determine the Gauss …

2011-10-13abs ↗pdf ↗

CEFOL uses deep learning for dynamic programming with recursive utility.

problem Challenges in solving dynamic programming problems with recursive utility.
method Introduces a separate neural network for certainty equivalent, uses first-order optimality conditions to learn value and policy functions.
result CEFOL achieves high accuracy in learning value and policy functions, matching VFI benchmarks.

New algorithm reduces online decision-making regret with efficient LP re-solving and parallel first-order method.

problem Worse regret guarantees and high computational cost of LP-based OLP algorithms.
method Combines LP-based and first-order OLP methods, re-solving LP subproblems periodically and using parallel first-order method.
result Achieves O(log(T/f)+f)\mathscr{O}(\log (T/f) + \sqrt{f}) regret, balancing computational efficiency and superior regret guarantee.

Unified bounds for iterative algorithms with Gaussian data matrices.

problem Establishing non-asymptotic bounds for iterative algorithms with Gaussian data.
method Explicit coupling between iterates and Gaussian process with deterministic covariance.
result Tight, dimension-free bounds for generalized first-order methods.

We construct a lagrangian geometric formulation for first-order field theories using the canonical structures of first-order jet bundles, which are taken as the phase spaces of the systems in consideration. First of all, we construct all the geometric structures associated with a first-order jet bundle and, using them,…

1995-05-17abs ↗pdf ↗

A new first-order sampler improves diffusion probabilistic model sampling quality.

problem The belief that first-order methods are inherently slower for diffusion probabilistic model sampling.
method A novel training-free, first-order sampler that approximates the forward-value evaluation via a one-step lookahead predictor.
result The proposed sampler provably approximates the ideal forward-value trajectory while retaining first-order convergence and can improve sample quality under the same NFE budget.

We establish that first-order methods avoid saddle points for almost all initializations. Our results apply to a wide variety of first-order methods, including gradient descent, block coordinate descent, mirror descent and variants thereof. The connecting thread is that such algorithms can be studied from a dynamical s…

2017-10-20abs ↗pdf ↗

Decides undecidability of equations and first-order theory for Seifert 3-manifold groups.

problem Decidability of equations and first-order theory in Seifert 3-manifold groups.
method Encoding Hilbert's tenth problem and using it to show undecidability.
result Undecidability of equations and first-order theory in Seifert 3-manifold groups with non-negative Euler characteristic.

First order methods can take extremely long to find global minima of non-convex functions.

problem Finding global minimizers of non-convex functions.
method Designing a family of non-convex functions and using statistical lower bounds for parameter estimation.
result First order methods can take exponential time to converge to a global minimizer.

Generalizes Hamiltonian theory for variational problems, applied to first order gravity.

problem Formulating Hamiltonian field theory for variational problems of general nature.
method Introduces a generalized Hamiltonian formalism without requiring a Hamiltonian section.
result Develops a novel multisymplectic Hamiltonian field theory for first order gravity.

New methods solve optimization problems with heavy-tailed noise, improving upon existing complexity bounds.

problem Optimization problems with heavy-tailed noise and weakly average smoothness.
method Normalized stochastic first-order methods with Polyak, multi-extrapolated, and recursive momentum.
result First-order oracle complexity results for finding approximate stochastic stationary points under heavy-tailed noise.

A new method speeds up quantum state estimation.

problem Exponential growth in sample size and dimension for quantum state tomography.
method Stochastic mirror descent with Burg entropy.
result Optimization error vanishes at a O((1/t)dlogt)O (\sqrt{ ( 1 / t ) d \log t }) rate.

New sampling method guarantees approximate first-order stationary points for non-convex functions.

problem Sampling from non-log-concave densities with non-convex potential functions.
method Averaged Langevin Monte Carlo with complexity analysis.
result Langevin Monte Carlo outputs a sample with ε-relative Fisher information after O(L²d²/ε²) iterations.

Develops a first-order interior-point method for solving constrained variational inequalities.

problem Solving constrained variational inequalities with nontrivial constraints.
method ADMM-based interior-point method for constrained VIs (ACVI).
result First-order interior-point method with global convergence guarantees for general cVI problems.

This paper is centred on solving differential equations by symmetry groups for first order ODEs and is in response to Starrett (2007). It also explores the possibility of averting the assumptions by Olver (2000) that, in practice finding the solutions of the linearized symmetry condition is usually a much more difficul…

2013-01-28abs ↗pdf ↗

DEO uses gradient information to escape saddle points in neural networks.

problem Training deep neural networks struggles with flat regions and saddle points.
method Dimer-Enhanced Optimization (DEO) uses gradient information to estimate curvature and escape saddle points.
result DEO improves training efficiency and performance compared to standard first-order methods.

OptEx accelerates first-order optimization with parallelized iterations.

problem Inefficiencies in first-order optimization algorithms for complex tasks.
method Approximately parallelized iterations using kernelized gradient estimation.
result OptEx achieves substantial efficiency improvements with an effective acceleration rate of Ω(N)Ω(\sqrt{N}).

A new method learns node embeddings for signed directed networks by capturing both first-order and high-order topologies.

problem Learning representative node embeddings for signed directed networks considering both first-order and high-order topologies.
method Proposes a decoupled variational embedding (DVE) method that leverages a specially designed auto-encoder structure to capture both first-order and high-order topologies.
result Extensive experiments on real-world datasets show the effectiveness of DVE in link sign prediction and node recommendation tasks.

We study the multiplicity sets of first order symbols associated with differential operators on two dimensional surfaces. This work is inspired by the phenomenon of conical refraction explained by the existence of singularities in the Fresnel hyper-surface for Maxwell's equations on an anisotropic crystal.

2013-11-04abs ↗pdf ↗

New inequalities help optimize first-order algorithms for statistical risk analysis.

problem Optimizing first-order iterative algorithms for statistical risk analysis.
method Introducing basic inequalities that connect implicit and explicit regularization.
result The basic inequalities translate the number of iterations into an effective regularization coefficient.

First-order ANIL learns shared representations even with overparametrization.

problem Lack of theoretical evidence for model-agnostic meta-learning (ANIL) learning shared representations.
method First-order ANIL with a linear two-layer network architecture, showing asymptotically low-rank solutions with overparametrization.
result First-order ANIL learns linear shared representations, even with overparametrization, and performs well in adaptation.