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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.

169,341 papers · 148 categories

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170340510680 · Jun 202019922001200920182026
48 results for logarithmic function

New algorithm achieves logarithmic regret for adversarial online control.

problem Online linear-quadratic control in systems with adversarial disturbances.
method Characterization of optimal offline control law, reduced to online learning with approximate advantage functions.
result First algorithm with logarithmic regret for arbitrary adversarial disturbance sequences.

Logarithmic regret achieved in RL with linear function approximation.

problem Achieving logarithmic regret in reinforcement learning with linear function approximation.
method LSVI-UCB for linear MDP assumption, UCRL-VTR for linear mixture MDP assumption.
result Logarithmic regret bounds established for RL with linear function approximation.

Through the main example of the Ornstein-Uhlenbeck semigroup, the Bakry-Emery criterion is presented as a main tool to get functional inequalities as Poincaré or logarithmic Sobolev inequalities. Moreover an alternative method using the optimal mass transportation, is also given to obtain the logarithmic Sobolev inequa…

2010-09-17abs ↗pdf ↗

Solutions near infinity to special Lagrangian equations are asymptotic to quadratic polynomials with logarithmic terms.

problem Solving special Lagrangian equations near infinity with specific conditions.
method Modified Kelvin transforms to characterize remainders in asymptotic expansions.
result Remainders in asymptotic expansions are characterized by a single smooth function in even dimensions and Cn1,αC^{n-1,α} in odd dimensions.

The study examines correlations of logarithms of integers at different scalings.

problem Analyzing pair correlations of logarithms of integers at various scalings.
method Examined correlations of logarithms of positive integers at different scalings, proving the existence of pair correlation functions.
result Level repulsion at linear scaling, total loss of mass at superlinear scalings, and Poissonian behavior at sublinear scalings.

Lueck expressed the Gromov norm of a knot complement in terms of an infinite series that can be computed from a presentation of the fundamental group of the knot complement. In this note we show that Lueck's formula, applied to torus knots, yields surprising power series expansions for the logarithm function. This gene…

2006-11-01abs ↗pdf ↗

Near-logarithmic regret per switch achieved for mixable/exp-concave losses.

problem Online optimization of mixable loss functions with dynamic environments.
method Online mixture framework using static solvers and hyper-expert creations.
result Near-logarithmic regret per switch with sub-polynomial complexity.

Study of conformal logarithmic Laplacian on sphere, connecting Yamabe problems and Sobolev spaces.

problem Yamabe-type problems and Sobolev spaces on the sphere.
method Detailed spectral analysis, conformal invariance, and Hilbert space introduction.
result Established precise connection between sphere and \(\mathbb{R}^N\) logarithmic Laplacian.

The paper explores the geometric structure of cost functions in multiple dimensions.

problem Understanding the geometric properties of cost functions in multidimensional settings.
method Analyzes the Hessian metric and geodesics in logarithmic and original coordinates.
result The geometry is one-dimensional in logarithmic coordinates but effectively (n1)(n-1)-dimensional in original coordinates.

New framework reduces minimax regret for high-dimensional data.

problem Minimizing regret in high-dimensional data with logarithmic loss.
method Developed envelope complexity framework and spike-and-tails prior.
result Achieves minimax regret within a factor of two over high-dimensional 1\ell_1-balls.

Paper proposes efficient cost functions for automated market makers in DeFi.

problem Inefficient and computationally complex cost functions in DeFi.
method Proposes and analyzes constant circle/ellipse based cost functions.
result Proposed cost functions are computationally efficient and robust against attacks.

The paper proves the concavity of pp-entropy power and applies it to functional inequalities.

problem Proving concavity of pp-entropy power on Riemannian manifolds.
method Analyzing the pp-heat equation on closed Riemannian manifolds with nonnegative Ricci curvature.
result New proofs and improvements of LpL^p-Euclidean Nash and Logarithmic Sobolev inequalities.

Optimal unimodal fitting for linear loss functions in a sequential, efficient manner.

problem Optimal unimodal transformation of univariate model scores under linear loss functions.
method Proposes a sequential approach to estimate the optimal rectangular fit for observed samples with each new sample.
result Sequential approach achieves optimal efficiency with logarithmic time complexity per iteration.

Study bounds for Brownian motion on manifolds with sticky boundary conditions.

problem Proving geometric bounds for Brownian motion on manifolds with sticky boundary conditions.
method Interpolation involving energy interactions between boundary and interior of the manifold.
result Explicit geometric bounds on Steklov eigenvalues, boundary trace operators, and boundary trace logarithmic Sobolev constants.

Research examines correlations of complex logarithms of lattice points, showing level repulsion and Poissonian behavior.

problem Analyzing correlations of complex logarithms of lattice points.
method Proving existence of pair correlation functions and examining behavior at various scalings.
result Level repulsion observed at linear scaling, Poissonian behavior at sublinear scalings.

Paper analyzes and improves adaptive gradient methods for optimization.

problem Improving optimization methods for deep neural networks.
method Analyzes and proposes variants of RMSProp and Adagrad for online convex optimization.
result Proposes SC-Adagrad and SC-RMSProp with logarithmic regret bounds for strongly convex functions.

New algorithm proves deep networks can learn better than shallow ones.

problem Understanding the power difference between shallow and deep neural networks.
method Identifying a class of Boolean functions and proving that logarithmic-depth networks can learn them efficiently using hierarchical reconstruction.
result First algorithmic separation between constant-depth and logarithmic-depth neural networks.

Paper analyzes and improves KL-regularized RL for LLMs with logarithmic regret.

problem Improving efficiency of RL fine-tuning for large language models.
method Optimism-based KL-regularized online contextual bandit algorithm with novel regret analysis.
result Achieves an O(ηlog(NRT)dR)\mathcal{O}\big(η\log (N_{\mathcal R} T)\cdot d_{\mathcal R}\big) logarithmic regret bound.

Study optimal investment and consumption in financial markets using Ornstein-Uhlenbeck process.

problem Optimal consumption/investment problem in financial markets with logarithmic utility.
method Stochastic dynamical programming method and Hamilton-Jacobi-Bellman (HJB) equation.
result Explicit solution to the HJB equation and optimal financial strategies constructed.

New optimal portfolios derived for power and logarithmic utilities under log-normal returns.

problem Optimal portfolio weights for power and logarithmic utilities under log-normal returns.
method Closed-form expressions derived for optimal portfolio weights, proving mean-variance efficiency.
result Both optimal portfolios are mean-variance efficient and belong to the feasible set.

Derives gradient estimate for a specific nonlinear parabolic equation on Finsler manifolds.

problem Derives gradient estimate for a nonlinear parabolic equation on Finsler manifolds.
method Leverages a new Laplacian comparison theorem to derive a Li-Yau type gradient estimate.
result Establishes a Li-Yau type gradient estimate for the Finslerian logarithmic Schrödinger equation.

The paper proves curvature rigidity for manifolds with specific scalar curvature bounds.

problem Curvature rigidity of manifolds with scalar curvature constraints.
method Power series expansions of logarithmic Sobolev and W-functionals, scalar curvature bounds, and isoperimetric profiles.
result The sectional curvature of a manifold is constant (K) if it satisfies scalar curvature and isoperimetric conditions.

Study functional inequalities on non-reversible Finsler manifolds.

problem Functional inequalities on non-reversible Finsler manifolds.
method Application of Bochner inequality and Γ-calculus.
result Dimensional versions of Poincare--Lichnerowicz, logarithmic Sobolev, and Sobolev inequalities hold for non-reversible metrics.

The paper addresses portfolio allocation with uncertain covariance matrices, finding a logarithmic risk dependence.

problem Portfolio allocation with uncertain covariance matrices.
method Calculates the expected value of CARA utility function over a distribution of covariance matrices, considering uncertainty in future returns and covariances.
result Marginalization introduces a logarithmic dependence on risk, leading to lower allocation levels for higher uncertainties.