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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 Semi-infinite Dimensions

Notes on Morse Homology, focusing on gradient flow lines and semi-infinite dimensional cases.

problem Exploring Morse Homology and its applications in semi-infinite dimensional spaces.
method Presentation of concepts in finite dimensional Morse Homology, with an eye towards generalization to semi-infinite dimensions.
result Intuition for Floer homology through finite dimensional Morse Homology concepts.

Develops new reinforcement learning methods for complex constrained decision-making problems.

problem Complex constrained decision-making problems with a continuum of constraints.
method Proposes semi-infinitely constrained Markov decision processes (SICMDPs) and two reinforcement learning algorithms: SI-CRL and SI-CPO.
result Demonstrates the effectiveness of SI-CRL and SI-CPO in solving complex sequential decision-making tasks.

Paper optimizes financial trading strategies under uncertain market conditions.

problem Guaranteeing robust positive expected profits in financial systems.
method Transformed semi-infinite constraints into structured policies and proposed a novel graphical approach.
result Demonstrated superior risk-adjusted returns and downside risk compared to conventional strategies.

We define a limiting slN\mathfrak{sl}_N Khovanov-Rozansky homology for semi-infinite positive multi-colored braids, and we show that this limiting homology categorifies a highest-weight projector for a large class of such braids. This effectively completes the extension of Cautis' similar result for infinite twist braid…

2019-04-19abs ↗pdf ↗

Polynomial-time convex optimization for CNNs with ReLU activations.

problem Training Convolutional Neural Networks (CNNs) with ReLU activations.
method Developed a convex analytic framework using semi-infinite duality to formulate equivalent convex optimization problems for CNN architectures.
result Proved that two-layer CNNs can be globally optimized via an 2\ell_2 norm regularized convex program.

We introduce a framework, twisted parametrized stable homotopy theory, for describing semi-infinite homotopy types. A twisted parametrized spectrum is a section of a bundle whose fibre is the category of spectra. We define these bundles in terms of modules over a stack of parametrized spectra and in terms of diagrams o…

2005-08-03abs ↗pdf ↗

Study geodesic trees and exceptional directions in FPP on hyperbolic groups.

problem Understanding the geometry and uniqueness of geodesics in FPP on hyperbolic groups.
method Analyzing random geodesic trees and exceptional directions in the context of FPP on hyperbolic groups.
result The set of exceptional directions has strictly smaller Hausdorff dimension than the boundary, and hence has measure zero.

Neural networks solve copositive programs, revealing insights into training problems.

problem Training two-layer vector-output ReLU neural networks.
method Convex analysis and copositive programming.
result Neural networks solve copositive programs, providing insights into training problems.

Neural networks can find financial arbitrage opportunities without needing market models.

problem Finding arbitrage opportunities in financial markets without using market models.
method Used neural networks to solve convex semi-infinite programs and detect arbitrage opportunities.
result Neural networks can detect model-free static arbitrage strategies in financial markets.

The paper identifies the best treatment to maximize NDPO, a key outcome in causal mediation analysis.

problem Identifying the treatment that maximizes the expected natural direct potential outcome (NDPO) in causal mediation analysis.
method Developed a fixed-confidence best-arm identification (BAI) algorithm based on the Track-and-Stop (TaS) framework, using a cutting-set method to solve a semi-infinite optimization problem.
result The proposed algorithm achieves sample-efficient identification with a high-probability correctness guarantee and asymptotic optimality.

This paper studies Brownian motion and heat kernel measure on a class of infinite dimensional Lie groups. We prove a Cameron-Martin type quasi-invariance theorem for the heat kernel measure and give estimates on the LpL^p norms of the Radon-Nikodym derivatives. We also prove that a logarithmic Sobolev inequality holds …

2009-02-14abs ↗pdf ↗

Rabinowitz Floer homology is the semi-infinite dimensional Morse homology associated to the Rabinowitz action functional used in the pioneering work of Rabinowitz. Gradient flow lines are solutions of a vortex-like equation. In this survey article we describe the construction of Rabinowitz Floer homology and its applic…

2010-01-24abs ↗pdf ↗

The paper develops bounds for multi-asset derivatives using option prices.

problem Computing model-free upper and lower bounds for multi-asset derivatives.
method Develops a fundamental theorem of asset pricing and superhedging duality, recasting the problem into a linear semi-infinite optimization problem and providing algorithms for exact computation.
result Provides ε\varepsilon-optimal upper and lower bounds for multi-asset derivatives, characterizing optimal pricing measures.

Using nonlinear pde techniques, we construct a new family of globally smooth tt* structures. This includes tt* structures associated to the (orbifold) quantum cohomology of a finite number of complex projective spaces and weighted projective spaces. The existence of such "magical solutions" of the tt* equations, namely…

2010-10-10abs ↗pdf ↗

We explore the robust replication of forward-start straddles given quoted (Call and Put options) market data. One approach to this problem classically follows semi-infinite linear programming arguments, and we propose a discretisation scheme to reduce its dimensionality and hence its complexity. Alternatively, one can …

2016-03-21abs ↗pdf ↗

Study non-parametric frequency-domain system identification from finite samples.

problem Frequency-domain system identification from limited data.
method Empirical Transfer Function Estimate (ETFE) under sub-Gaussian colored noise and stability assumptions.
result ETFE estimates are concentrated around true values with a finite-sample rate of Ntot1/3N_{\mathrm{tot}}^{-1/3} for all frequencies in the H \mathcal{H}_{\infty} norm.

This is an introduction to the subject of the differential topology of the space of smooth loops in a finite dimensional manifold. It began as the background notes to a series of seminars given at NTNU and subsequently at Sheffield. I am posting them in the hope that they will be useful to people wishing to know a litt…

2005-10-05abs ↗pdf ↗

Study on moduli spaces of Seiberg-Witten equations on manifolds with boundary.

problem Analyzing moduli spaces of Seiberg-Witten equations on manifolds with boundary.
method General regularity theorem, strong unique continuation principle, and gluing theorem for Dirac operators; smoothness of restriction map.
result Proves moduli spaces are Hilbert manifolds and have semi-infinite-dimensionality properties.

We study the regularized determinant of the Laplacian as a functional on the space of Mandelstam diagrams (noncompact translation surfaces glued from finite and semi-infinite cylinders). A Mandelstam diagram can be considered as a compact Riemann surface equipped with a conformal flat singular metric ω2|ω|^2, where ωω

2013-12-01abs ↗pdf ↗

We consider the problem of classifying data manifolds where each manifold represents invariances that are parameterized by continuous degrees of freedom. Conventional data augmentation methods rely upon sampling large numbers of training examples from these manifolds; instead, we propose an iterative algorithm called M…

2017-05-28abs ↗pdf ↗

We study the problem of the execution of a moderate size order in an illiquid market within the framework of a solvable Markovian model. We suppose that in order to avoid impact costs, a trader decides to execute her order through a unique trade, waiting for enough liquidity to accumulate at the best quote. We find tha…

2014-09-30abs ↗pdf ↗

In this paper, the Weierstrass technique for harmonic maps S^2 -> CP^(N-1) is employed in order to obtain surfaces immersed in multidimensional Euclidean spaces. It is shown that if the CP^(N-1) model equations are defined on the sphere S^2 and the associated action functional of this model is finite, then the generali…

2008-02-08abs ↗pdf ↗

We develop robust pricing and hedging of a weighted variance swap when market prices for a finite number of co--maturing put options are given. We assume the given prices do not admit arbitrage and deduce no-arbitrage bounds on the weighted variance swap along with super- and sub- replicating strategies which enforce t…

2010-01-15abs ↗pdf ↗

We introduce a new method for estimating the support size of an unknown distribution which provably matches the performance bounds of the state-of-the-art techniques in the area and outperforms them in practice. In particular, we present both theoretical and computer simulation results that illustrate the utility and p…

2019-01-22abs ↗pdf ↗

Paper proposes a robust method for inferring parameters in multiobjective optimization.

problem Uncertainty in hypothetical decision-making problem, data quality, and parameter space.
method Wasserstein distributionally robust approach for inverse multiobjective optimization.
result WRO-IMOP minimizes worst-case expected loss over a Wasserstein ball of distributions.

This paper tackles robust control of noisy systems with uncertain distributions.

problem Optimal control of sampled-data stochastic systems with multiplicative noise and distributional ambiguity.
method Develops a convex relaxation to handle the ``concave-max'' geometry and derives a probabilistic performance guarantee.
result Derives an explicit, non-asymptotic bound on the duality gap and proves robust viability conditions.

Convex optimization refines neural network training, improving model performance and reducing hyperparameter sensitivity.

problem Training deep neural networks using non-convex optimization methods often leads to suboptimal solutions and requires extensive tuning.
method Formulate neural network training as convex programs with regularization terms, leveraging sparse recovery models and semi-infinite programming theory.
result Convex models can achieve global optima and outperform traditional non-convex methods, with improved robustness to hyperparameters.

Defines and classifies Thurston geometries and connects simplicial volume to Kodaira dimension.

problem Classifying Thurston geometries and understanding their properties.
method Introduces an axiomatic definition for the Kodaira dimension and studies its compatibility with traditional notions.
result Establishes a connection between the simplicial volume and the holomorphic Kodaira dimension, showing implications for smooth Kähler 3-folds.

Paper relates asymptotic dimension to cofinal dimension using coarse proximities.

problem Relating asymptotic dimension to cofinal dimension in metric spaces.
method Introducing coarse proximities and inverse limit constructions.
result Asymptotic dimension is bounded by coarse cofinal dimension and cofinal dimension of Higson corona.

Our goal in this paper is to develop an effective estimator of fractal dimension. We survey existing ideas in dimension estimation, with a focus on the currently popular method of Grassberger and Procaccia for the estimation of correlation dimension. There are two major difficulties in estimation based on this method. …

2013-12-09abs ↗pdf ↗

We introduce a new quasi-isometry invariant of metric spaces called the hyperbolic dimension, hypdim, which is a version of the Gromov's asymptotic dimension, asdim. The hyperbolic dimension is at most the asymptotic dimension, however, unlike the asymptotic dimension, the hyperbolic dimension of any Euclidean space R^…

2004-04-29abs ↗pdf ↗

In the first part of the paper we show how to relate several dimension theories (asymptotic dimension with Higson property, asymptotic dimension of Gromov, and capacity dimension of Buyalo \cite{Buyalo1}) to Nagata-Assouad dimension. This is done by applying two functors on the Lipschitz category of metric spaces: micr…

2006-01-10abs ↗pdf ↗

This paper studies three aspects around dimension datum: (1), a generalization of the dimension datum, which we call the tau-dimension datum; (2), dimension data of disconnected subgroups; (3), compactness of isospectral sets of normal homogeneous spaces.

2018-03-16abs ↗pdf ↗

Random walks on Fuchsian Schottky groups have harmonic measures with lower dimension.

problem Understanding the dimensionality of harmonic measures for random walks.
method Analyzing finite range random walks on Fuchsian Schottky groups.
result Harmonic measures have dimension strictly less than the limit set's Hausdorff dimension.