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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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48 results for Geometric analysis

This is a guided tour through some selected topics in geometric analysis. We have chosen to illustrate many of the basic ideas as they apply to the theory of minimal surfaces. This is, in part, because minimal surfaces is, if not the oldest, then certainly one of the oldest areas of geometric analysis dating back to Eu…

2003-09-01abs ↗pdf ↗

This paper reviews discrete curvature models for geometric data analysis.

problem Capturing intrinsic geometric structure in diverse data representations.
method Comprehensive review of discrete curvature models from Riemannian and metric geometry perspectives.
result Systematic pipeline for curvature-driven data analysis and learning.

Geometric analysis improves convergence of variational inference.

problem Challenges in analyzing convergence of variational inference due to non-convexity and non-smoothness.
method Exploits exponential family structure and Bregman divergences to geometrically analyze the optimization landscape.
result Establishes non-asymptotic convergence rates for gradient descent algorithms.

Machine learning methods struggle with geometric data, but shape space analysis provides a framework for studying and analyzing geometric variability.

problem Machine learning methods struggle with geometric data
method Shape space analysis provides a mathematical and computational framework
result Characterizes shape variability, compares geometric objects, and analyzes structural trajectories

Study uses geometric algebra to analyze credit cycles, revealing dangerous feedback loops.

problem Understanding and predicting dangerous feedback loops in credit cycles.
method Represent economic states as multi-vectors in Clifford algebra, focusing on bivector elements for rotational coupling.
result Geometric relationship between unemployment and credit contraction shifts from simple correlation to dangerous rotational dynamics during crises.

The paper explores parabolic regularity in geometric variational analysis.

problem Developing calculus rules and computation formulas for second-order generalized differential constructions.
method Introducing and applying the concept of parabolic regularity to geometric aspects of second-order variational analysis.
result Established new calculus rules and computation formulas for second-order generalized differential constructions.

Framework for mapping unknown environments using biobot agents and topological data analysis.

problem Mapping unknown environments with minimal sensing and localization constraints.
method Local interactions among biobot agents create encounter graphs, which are used for manifold learning and topological data analysis.
result The proposed metric converges to geodesic distances in the underlying manifold, and the topological features are robust.

Paper compares stock price prediction models using Heston and Geometric Brownian Motion.

problem Predicting stock prices accurately.
method Developed Heston and Geometric Brownian Motion models using Ito's lemma and Euler-Maruyama methods.
result Models outperform statistical indicators in predicting stock prices.

Paper formalizes multi-dimensional FSD using geometric methods.

problem Complex measure theory and calculus barriers to formalization in proof assistants.
method Geometric framework for first-order stochastic dominance in N dimensions.
result Geometric approach bypasses complex integration theory for direct comparison of survival probabilities.

Solves optimal liquidation problem for stock price following geometric Brownian motion.

problem Optimal liquidation problem for stock price process following geometric Brownian motion.
method Functional analysis tools; working in terms of cash.
result Explicit solution to the problem, extending to stochastic drift.

Refines geometric center of mass analysis for Einstein field equations.

problem Analyzing the geometric center of mass of Willmore surfaces in initial data for Einstein field equations.
method Refined Lyapunov-Schmidt analysis to study geometric center of mass of area-constrained Willmore surfaces.
result The geometric center of mass agrees with the Hamiltonian center of mass under specific conditions.

Study geometric quantum confinement on special incomplete Riemannian manifolds.

problem Characterize quantum confinement on Grushin-type manifolds.
method Constant-fibre direct integral scheme combined with Weyl's analysis.
result Fully characterizes essential self-adjointness of Laplace-Beltrami operator.

Unified geometric approach to image reconstruction from incomplete data.

problem Reconstruction of hidden structures from incomplete data.
method Geometric decomposition of configuration spaces into invariant foliations and moment maps, combining Vaisman and Neifeld's insights.
result Noise-resistant framework for robust computational reconstruction in imaging and structural analysis.

Novel analysis of neural networks using geometric algebra and convex optimization.

problem Understanding the inner workings of deep neural networks.
method Geometric (Clifford) algebra and convex optimization.
result Optimal weights are given by the wedge product of training samples.

The authors study the method of scaling in the context of the study of automorphism groups of complex domains in multiple dimensions. Various types of scaling techniques are compared and contrasted. Applications are given in a number of areas of complex geometric analysis. Relations with other parts of mathematics are …

2006-10-24abs ↗pdf ↗

GeoTop resolves topological ambiguity in diagnostic imaging using geometric-topological analysis.

problem Topological equivalence between benign and malignant structures in diagnostic images.
method Combines Topological Data Analysis and Lipschitz-Killing Curvatures to resolve ambiguity.
result Achieves 3.6% accuracy improvement and reduces false positives/negatives by 15-18%.

Study on directed graphs using Ricci curvature, extending previous undirected graph results.

problem Generalization of Ricci curvature for directed graphs.
method Introducing a new Ricci curvature for directed graphs using mean transition probability kernel.
result Several geometric and spectral properties of directed graphs under a lower Ricci curvature bound.

Expands Bredon's trick for applications in geometry and topology.

problem Local-to-global extension principles in geometric and topological contexts.
method Novel applications and frameworks for stratified pseudomanifolds, Ricci flow, and persistent homology.
result Establishes Bredon's trick as a unifying framework.

Geometric scattering for graph data enhances feature retention and classification.

problem Tackling the generalization of scattering transforms to graph data.
method Analogous to ConvNets, we develop geometric scattering for graph data, focusing on feature stability under graph deformations.
result Extracted features retain informative variability and relations in graph data, aiding classification and exploration.

The paper contrasts different convergence notions in geometric analysis.

problem Exploring discrepancies between various convergence concepts in geometric analysis.
method Examples and proof of a theorem requiring specific bounds on warping functions.
result Warped product manifolds can have different convergence limits even if the warping functions converge in LpL^p.