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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.

168,695 papers · 148 categories

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2525047561,008 · Jun 202019922001200920172026
48 results for linear complex structure

The paper explores linear generalised complex structures over vector bundles.

problem Understanding holomorphic vector bundles in a generalized geometry context.
method Adapted linear splitting and equivalence to C\mathbb C-multiplication and C\mathbb C-Lie algebroid structure.
result Generalised complex Lie algebroids are expressed as complex conjugated Lie bialgebroids.

We introduce linear Dirac and generalized complex structures on Cartan geometries and give criteria for Dirac subalgebras of $\frkg\ltimes\frkg^*$ representing Dirac structures on a Cartan geometry. We prove that there is a bijection between the linear generalized structures on a torsion free Cartan geometry and the eq…

2012-04-26abs ↗pdf ↗

In this paper we investigate fiber-wise linear complex Banach sub-Poisson structures defined canonically by the structure of a W*-algebra M. In particular we show that these structures are arranged in the short exact sequence of complex Banach sub-Poisson VB-groupoids with the groupoid of partially invertible elements …

2017-03-03abs ↗pdf ↗

We give a local classification of generalized complex structures. About a point, a generalized complex structure is equivalent to a product of a symplectic manifold with a holomorphic Poisson manifold. We use a Nash-Moser type argument in the style of Conn's linearization theorem.

2012-01-23abs ↗pdf ↗

Combining neural networks and multiscale decomposition for financial market analysis.

problem Financial markets' complexity and mainstream models' limitations in capturing non-linear structures.
method Neural networks for non-linear associations combined with multiscale decomposition.
result Improved understanding of financial market data substructures.

New framework allows reinforcement learning with polynomial sample complexity.

problem Generalization in reinforcement learning with function approximation.
method Introduces Bilinear Classes, a structural framework for RL.
result Polynomial sample complexity for Bilinear Classes, matching best known bounds.

Graphical notation simplifies complex polynomial constraints in linear models.

problem Complex polynomial constraints in linear structural equation models are impractical.
method Developed a graphical notation to represent these constraints.
result The graphical notation simplifies the representation of many polynomial constraints.

In the present paper, the (HM,S,T)(HM',S,T)-Cartan connections on pseudo-Finsler manifolds, introduced by A. Bejancu and H.R. Farran, are obtained by the natural almost complex structure arising from the nonlinear connection HMHM'. We prove that the natural almost complex linear connection associated to a (HM,S,T)(HM',S,T)-Cartan …

2006-09-06abs ↗pdf ↗

In generalized complex geometry, we revisit linear subspaces and submanifolds that have an induced generalized complex structure. We give an expression of the induced structure that allows us to deduce a smoothness criteria, we dualize the results to submersions and we make a few comments on generalized complex mapping…

2014-12-03abs ↗pdf ↗

On asymptotically complex hyperbolic (ACH) Einstein manifolds, we consider a certain variational problem for almost complex structures compatible with the metric, for which the linearized Euler-Lagrange equation at Kähler-Einstein structures is given by the Dolbeault Laplacian acting on (0,1)(0,1)-forms with values in the…

2018-12-23abs ↗pdf ↗

The linear slice of quasi-Fuchsian once-punctured torus groups is defined by fixing the complex length of some simple closed curve to be a fixed positive real number. It is known that the linear slice is a union of disks, and it always has one standard component containing Fuchsian groups. Komori and Yamashita proved t…

2014-12-29abs ↗pdf ↗

Over the last decade, both the neural network and kernel adaptive filter have successfully been used for nonlinear signal processing. However, they suffer from high computational cost caused by their complex/growing network structures. In this paper, we propose two random Euler filters for complex-valued nonlinear filt…

2018-01-02abs ↗pdf ↗

Deep neural networks are composed of layers of parametrised linear operations intertwined with non linear activations. In basic models, such as the multi-layer perceptron, a linear layer operates on a simple input vector embedding of the instance being processed, and produces an output vector embedding by straight mult…

2019-05-03abs ↗pdf ↗

A new approach uses circuit topology to study complex polymer interactions.

problem Understanding structural phase transitions in entangled polymer systems.
method Braided circuit topology framework for multiple-chain systems.
result Circuit topological motif fractions are effective order parameters for structural transitions.

Normal forms of almost complex structures in a neighborhood of pseudoholomorphic curve are considered. We define normal bundles of such curves and study the properties of linear bundle almost complex structures. We describe 1-jet of the almost complex structure along a curve in terms of its Nijenhuis tensor. For pseudo…

2001-05-16abs ↗pdf ↗

Rectified flows achieve optimal sample complexity for generating data.

problem Generating high-quality data samples efficiently.
method Rectified flows constrain transport trajectories to be linear, enabling efficient sampling.
result Achieve sample complexity of ildeO(ε2) ilde{O}(\varepsilon^{-2}), matching optimal rate for mean estimation.

This paper analyzes error in SKI for Gaussian Processes, providing conditions for linear time inference.

problem Lack of rigorous theoretical error analysis for SKI.
method Proved error bounds for SKI Gram matrix, examined error effects, provided practical guidelines.
result Identified two dimensionality regimes for SKI's scalability-accuracy trade-offs.

In the present work the local form of certain Calabi-Yau metrics possessing a local Hamiltonian Killing vector is described in terms of a single non linear equation. The main assumptions are that the complex (3,0)(3,0)-form is of the form eikΨ~e^{ik}\widetildeΨ, where Ψ~\widetildeΨ is preserved by the Killing vector, and tha…

2010-04-22abs ↗pdf ↗

The paper explores Kähler and anti-Kähler structures on quasi-statistical manifolds.

problem Investigating Kähler and anti-Kähler structures on quasi-statistical manifolds.
method Analyzing conditions for integrability of almost complex structures and defining Kähler and anti-Kähler manifolds.
result Conditions for (Nˊ,h,abla,L)(\acute{N},h, abla ,L) to be an anti-Kähler manifold are identified.

Learning to control linear systems is statistically hard, especially for underactuated systems.

problem Statistical difficulty of learning to control linear systems, especially underactuated ones.
method Utilized minimax lower bounds and structural assumptions to prove learning complexity can be exponential.
result Learning complexity can be at most exponential with the controllability index of the system.

Sharp LL^\infty estimates proved for complex Monge-Ampère equations.

problem Proving sharp LL^\infty estimates for complex Monge-Ampère equations.
method PDE proof covering fixed and degenerating background metrics, extends to general fully non-linear equations.
result Sharp LL^\infty estimates proved for complex Monge-Ampère equations.

Introduces Fock bundles for studying surface group character varieties.

problem Character varieties of surface groups without fixed complex structures.
method Introduces Fock bundles as smooth principal bundles with special adjoint-valued 1-forms, constructs canonical connections, and solves non-linear PDEs.
result Explicit solutions for Fock bundles in the Fuchsian locus map to the Hitchin component.

New AMP algorithms for rotationally invariant models with reduced complexity.

problem Signal estimation in generalized linear models with arbitrary spectral design matrices.
method Rotationally invariant approximate message passing (AMP) algorithms.
result Performance close to Vector AMP with significantly lower complexity.

Study Poisson cohomology and linearize Lie algebra structures.

problem Linearize Poisson structures on sl2(C)\mathfrak{sl}_2(\mathbb{C}).
method Calculate Poisson cohomology, construct homotopy operators, develop Nash-Moser method.
result Show that Poisson structures linearizable at zero are flat.

CoLA automates efficient numerical linear algebra for complex matrix structures.

problem Efficiently solving large-scale linear algebra problems with complex matrix structures.
method Combining linear operator abstraction with compositional dispatch rules.
result Automatic and efficient numerical algorithms for various linear algebra operations.

Study analyzes landscape complexity of empirical loss functions with correlated data.

problem Understanding the complexity of loss landscapes in machine learning with structured data.
method Kac-Rice formula and random matrix theory applied to high-dimensional empirical loss functions.
result Characterizes the average number of critical points in loss functions with structured data.

Study on learning sparse fixed-structure Gaussian Bayesian networks with near-optimal sample complexity.

problem Learning a fixed-structure Gaussian Bayesian network up to a bounded error in total variation distance.
method Analysis of node-wise least squares regression and introduction of BatchAvgLeastSquares and CauchyEst algorithms.
result BatchAvgLeastSquares and CauchyEstTree have near-optimal sample complexity.

Study Lie algebroid connections on principal bundles over complex projective varieties.

problem Existence and properties of Lie algebroid connections on principal bundles.
method Definition and study of Lie algebroid valued connections on holomorphic principal G-bundles, investigation of existence criteria.
result Investigation of criteria for existence of Lie algebroid connections on principal G-bundles over smooth complex projective curves.

The paper extends complex structure existence to manifolds of dimension 8.

problem Existence of complex structures on open manifolds of various dimensions.
method Construction of Γ_n^C structures on CP^n and application of obstruction theory.
result The homology of BΓ_n^C is derived, leading to a theorem about complex structures.

In this work, we propose a robust approach to design distributed controllers for unknown-but-sparse linear and time-invariant systems. By leveraging modern techniques in distributed controller synthesis and structured linear inverse problems as applied to system identification, we show that near-optimal distributed con…

2019-09-21abs ↗pdf ↗

Linear-Core Surrogates combine fast optimization and statistical efficiency in classification and structured prediction.

problem The trade-off between smoothness and margin-based losses in classification and structured prediction.
method Linear-Core (LC) Surrogates, a family of convex loss functions that stitch a linear core to a smooth tail.
result LC Surrogates achieve fast linear consistency rates while maintaining differentiability and strict HH-consistency bounds.

The space ML(F) of measured geodesic laminations on a given closed hyperbolic surface F has a canonical linear structure arising in fact from different sources in 2-dimensional hyperbolic (earthquake theory) or complex projective (grafting) geometry as well as in (2+1) Lorentzian one (globally hyperbolic spacetimes of …

2005-05-10abs ↗pdf ↗

Develops Hodge theory for boundary-value problems on general geometric structures.

problem Solvability and uniqueness conditions for linearized overdetermined boundary-value problems.
method Introduces elliptic pre-complex and order-reduction property to generalize Hodge theory.
result Provides tools to study cohomology explicitly for general geometric structures.

Given a complex structure JJ on a real (finite or infinite dimensional) Hilbert space HH, we study the geometry of the Lagrangian Grassmannian Λ(H)Λ(H) of HH, i.e. the set of closed linear subspaces LHL\subset H such that J(L)=L.J(L)=L^\perp. The complex unitary group U(HJ)U(H_J), consisting of the elements of the orthogona…

2008-08-16abs ↗pdf ↗