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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.

168,695 papers · 148 categories

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4691137182 · Jun 202019922001200920172026
48 results for lattice basis reduction

The asymptotic lattices and their transformations are studied within the line geometry approach. It is shown that the discrete asymptotic nets are represented by isotropic congruences in the Plucker quadric. On the basis of the Lelieuvre-type representation of asymptotic lattices and of the discrete analog of the Mouta…

1999-09-16abs ↗pdf ↗

We consider a high dimensional linear regression problem where the goal is to efficiently recover an unknown vector ββ^* from nn noisy linear observations Y=Xβ+WRnY=Xβ^*+W \in \mathbb{R}^n, for known XRn×pX \in \mathbb{R}^{n \times p} and unknown WRnW \in \mathbb{R}^n. Unlike most of the literature on this model we make no spa…

2018-03-18abs ↗pdf ↗

Reduces learning periodic neural networks to lattice problems, proving hardness under cryptographic assumptions.

problem Learning single periodic neurons in noisy environments.
method Reduction to worst-case lattice problems, using LLL algorithm.
result Polynomial-time algorithms for learning these functions are hard under cryptographic assumptions.

In this paper we prove that every H-type Lie algebra possesses a basis with respect to which the structure constants are integers. Existence of such an integral basis implies via the Mal'cev criterion that all simply connected H-type Lie groups contain cocompact lattices. Since the Campbell-Hausdorff formula is very si…

2001-01-28abs ↗pdf ↗

This article considers algorithmic and statistical aspects of linear regression when the correspondence between the covariates and the responses is unknown. First, a fully polynomial-time approximation scheme is given for the natural least squares optimization problem in any constant dimension. Next, in an average-case…

2017-05-19abs ↗pdf ↗

The lattice cohomology of a plumbed 3--manifold MM associated with a connected negative definite plumbing graph is an important tool in the study of topological properties of MM, and in the comparison of the topological properties with analytic ones when MM is realized as complex analytic singularity link. By defini…

2013-02-19abs ↗pdf ↗

We introduce the Koenigs lattice, which is a new integrable reduction of the quadrilateral lattice (discrete conjugate net) and provides natural integrable discrete analogue of the Koenigs net. We construct the Darboux-type transformations of the Koenigs lattice and we show permutability of superpositions of such trans…

2002-03-07abs ↗pdf ↗

Let G be a lattice in PSL(2,C). The pro-normal topology on G is defined by taking all cosets of non-trivial normal subgroups as a basis. This topology is finer than the pro-finite topology, but it is not discrete. We prove that every finitely generated subgroup H<G is closed in the pro-normal topology. As a corollary w…

2005-04-21abs ↗pdf ↗

Group lattices (Cayley digraphs) of a discrete group are in natural correspondence with differential calculi on the group. On such a differential calculus geometric structures can be introduced following general recipes of noncommutative differential geometry. Despite of the non-commutativity between functions and (gen…

2002-12-18abs ↗pdf ↗

In this paper we investigate flows on discrete curves in $\C^2$, $\CP^1$, and $\C$. A novel interpretation of the one dimensional Toda lattice hierarchy and reductions thereof as flows on discrete curves will be given.

2002-08-23abs ↗pdf ↗

In the framework of geometric quantization we extend the Bohr-Sommerfeld rules to a full quantization theory which resembles Heisenberg's matrix theory. This extension is possible because Bohr-Sommerfeld rules not only provide an orthogonal basis in the space of quantum states, but also give a lattice structure to this…

2012-07-05abs ↗pdf ↗

Paper improves distributed mean estimation and variance reduction without relying on input norm.

problem Distributed mean estimation and variance reduction with large input norms.
method Quantization and lattice theory connection for improved error bounds.
result Output error bounds depend only on input distance, not norm.

Quantum codes on hyperbolic lattices outperform Euclidean ones with higher rates and lower overhead.

problem Improving quantum error correction performance with hyperbolic lattices.
method Unified framework using Hyperbolic Cycle Basis algorithm for CSS codes construction and benchmarking.
result Achieved higher encoding rates and lower qubit overhead in hyperbolic quantum error correction codes.

The main aim of this paper is the description of a large class of lattices in some nilpotent Lie groups, sometimes filiformes, carrying a flat left invariant linear connection anf often a left invariant symplectic form. As a consequence we obtain an infinity of, non homeomorphic, compact affine or symplectic manifolds.…

2012-08-13abs ↗pdf ↗

The paper introduces a method to decorrelate circular coordinates using lattice reduction.

problem Geometric correlation between circle-valued maps when multiple cohomology classes are used.
method Systematic procedure using the Lenstra--Lenstra--Lovász algorithm for constructing low energy torus-valued maps.
result A method to obtain less correlated maps from cohomology classes using integer linear combinations.

Classifies actions of tori on manifolds up to diffeomorphisms.

problem Classifying actions of tori on manifolds up to diffeomorphisms.
method Using triples (Q, λ, c) to classify actions, where Q is a manifold-with-corners, λ is a unimodular labelling, and c is a cohomology class.
result Classifies locally standard smooth actions of T up to equivariant diffeomorphisms.

We study representations of lattices of PU(m,1) into PU(n,1). We show that if a representation is reductive and if m is at least 2, then there exists a finite energy harmonic equivariant map from complex hyperbolic m-space to complex hyperbolic n-space. This allows us to give a differential geometric proof of rigidity …

2003-09-11abs ↗pdf ↗

We present a family of complete acyclic Morse matchings on the face lattice of a hypersimplex. Since a hypersimplex is a convex polytope, there is a natural way to form a CW complex from its faces. In a future paper we will utilize these matchings to classify every subcomplex whose reduced homology groups are concentra…

2012-11-07abs ↗pdf ↗

Dimension reduction of multivariate data supervised by auxiliary information is considered. A series of basis for dimension reduction is obtained as minimizers of a novel criterion. The proposed method is akin to continuum regression, and the resulting basis is called continuum directions. With a presence of binary sup…

2016-06-20abs ↗pdf ↗

Adaptive neural networks learn functional data bases for improved performance.

problem Applying deep learning to functional data is challenging due to high dimensionality.
method Proposes adaptive neural networks with Basis Layers that learn relevant basis functions.
result Empirically outperforms other neural network approaches across various tasks.

Improves MARS for nonparametric multivariate regression with dimension reduction.

problem High number of basis functions in MARS for high-order interactions.
method Linear combinations of covariates for dimension reduction, facilitating gradient calculation and eigen-analysis for estimation.
result Asymptotic theory and numerical studies show improved performance over MARS.

Paper develops a two-population model to assess longevity basis risk.

problem Mismatch between hedger's liability and hedging instrument causes longevity basis risk.
method Develops a two-population mortality model using Lee-Carter model and renewal process.
result Proposed model provides significant risk reduction when mortality jumps and sampling risk are considered.

American put options are among the most frequently traded single stock options, and their calibration is computationally challenging since no closed-form expression is available. Due to the higher flexibility in comparison to European options, the mathematical model involves additional constraints, and a variational in…

2016-11-19abs ↗pdf ↗

This work ensures stability in POD basis interpolation for pMOR in hyperelasticity.

problem Stability of POD basis interpolation on Grassmann manifolds for pMOR in hyperelasticity.
method Stability conditions derived from Grassmannian Exponential map and principal angles.
result Explicit stability conditions for practical pMOR applications and non-monotonic error behavior.

The paper explores polysymplectic structures and their reductions in field theories.

problem Invariance of Lagrangian and Hamiltonian field theories under symmetry groups.
method Application of polysymplectic reduction theorem for both Lagrangian and Hamiltonian field equations.
result Identification and relation of polysymplectic structures through Routhian function and Legendre transformation.

Geometrically represents path integral reduction Jacobian for interacting systems.

problem Quantizing a model mechanical system with dependent coordinates.
method Geometric representation using scalar curvature and Christoffel symbols in a nonholonomic basis.
result Found a geometric representation for the path integral reduction Jacobian.

Lossy compression of statistical data using quantum annealing.

problem Efficiently compressing statistical floating-point data.
method Representation learning with binary variables, classical optimization of basis vectors, quantum annealing for coefficients, bias correction.
result Quantum annealing shows promising results with 3.5x better compression than neural-network autoencoders.

Google's multilingual speech recognition system combines low-level acoustic signals with language-specific recognizer signals to better predict the language of an utterance. This paper presents our experience with different signal combination methods to improve overall language identification accuracy. We compare the p…

2019-10-21abs ↗pdf ↗

BasisVAE combines VAE and clustering for tabular data analysis.

problem Lack of insights in tabular high-dimensional data analysis.
method Combines VAE with probabilistic clustering prior for joint dimensionality reduction and clustering.
result Learned one-hot basis function representation for translation-invariant features.

We consider the multi-bump solutions of the following fractional Nirenberg problem \begin{equation}\label{01} (-Δ)^s u=K(x)u^{\frac{n+2s}{n-2s}}, \;\;\;\;u>0\;\;\text{ in }\mathbb{R}^n, \end{equation} where s(0,1)s\in (0,1) and n>2+2sn>2+2s. If KK is a periodic function in some kk variables with 1k<n2s21\leq k<\frac{n-2s}2, we pr…

2016-12-13abs ↗pdf ↗