Research
On-device research index

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

Trend · papers per month

243486729972 · Jun 202019922001200920172026
48 results for Complex functions

Characterizes complex Hessian equations for bounded energy functions.

problem Understanding degenerate complex Hessian equations for bounded energy functions.
method Proving sublevel set estimates and using Sobolev inequalities.
result Characterization of degenerate complex Hessian equations for bounded (p,m)(p,m)-energy functions.

Complex functional maps link tangent bundles, preserving orientation and angles.

problem Linking tangent bundles for orientation-aware correspondence.
method Endow tangent bundles with complex structures to enable robust transfer of tangent vector fields.
result Establishes orientation-aware correspondence without relying on descriptors or extra regularization.

Constructs explicit harmonic functions and morphisms on complex and quaternionic Grassmannians.

problem Creating explicit solutions for pp-harmonic functions and harmonic morphisms.
method Using joint eigenfunctions of the Laplace-Beltrami and conformality operators.
result Induces solutions on dual non-compact Riemannian symmetric spaces.

We propose the study of a conformally invariant functional for surfaces of complex projective plane which is closely related to the classical Willmore functional. We show that minimal surfaces of complex projective plane are critical for this functional and construct some minima for it via the twistors spaces of comple…

2000-02-18abs ↗pdf ↗

In the previous papers \cite{L1, L2} the author constructed Mabuchi and Aubin-Yau functionals over any complex surfaces and three-folds, respectively. Using the method in \cite{L2}, we construct those functionals over any complex manifolds of the complex dimension bigger than or equal to 2.

2010-04-05abs ↗pdf ↗

Complexity measures for neural nets with general activations using path-based norms.

problem Control complexity of neural networks with arbitrary activation functions.
method Approximate general activations with ReLU networks and derive path-based norms for complexity control.
result Preliminary analyses of function spaces and regularized estimators.

The paper explores complex Poisson structures on smooth functions in complex manifolds.

problem Exploring complex Poisson structures on smooth functions in complex manifolds.
method Considering structures of complex Poisson brackets generated by a (1,1)(1,1)-form.
result Examples of complex Poisson structures are provided in $\C^\ast$.

Paper proposes LANN to measure model complexity of neural networks with curve activation functions.

problem Measuring model complexity of neural networks with curve activation functions.
method Proposes LANN, a piecewise linear framework to approximate curve activation functions, and derives complexity measure based on the number of linear regions.
result Demonstrates positive correlation between overfitting and model complexity during training.

We obtain rigidity and gluing results for the Morse complex of a real-valued Morse function as well as for the Novikov complex of a circle-valued Morse function. A rigidity result is also proved for the Floer complex of a hamiltonian defined on a closed symplectic manifold (M,ω)(M,ω) with $c_{1}|_{π_{2}(M)}=[ω]|_{π_{2}(M)…

2001-07-30abs ↗pdf ↗

Maxout networks show similar complexity issues as ReLU networks.

problem Understanding the complexity of maxout networks and decision boundaries.
method Analyzing the parameter space and decision boundaries, obtaining lower bounds, and investigating initialization procedures.
result Maxout networks exhibit a wide range of complexity, similar to ReLU networks.

CVNNs improve performance in tasks with complex-valued inputs.

problem Improving performance in tasks with complex-valued inputs.
method Analyze the approximation properties of complex-valued neural networks (CVNNs).
result Quantitative approximation bounds for CVNNs, showing error scales as mk/(2n)m^{-k/(2n)}.

The paper uses complex-valued functions to simplify plane differential geometry and kinematics.

problem Simplifying complex problems in plane differential geometry and kinematics.
method Consistent use of complex-valued functions of a real variable.
result Derives results in a particularly simple, uniform, and transparent way.

Unified theory of deep neural networks with diverse activations.

problem Understanding the relationship between depth and complexity in deep neural networks.
method Developed a unified function space theory for deep networks with various activations.
result Unified theory provides meaningful complexity for deep networks with diverse activations.

Extends holomorphic functions on complex manifolds to larger spaces.

problem Extending holomorphic functions on complex manifolds.
method Proving the existence of a larger space B(S,X)B(S,X) for continuous maps that allows holomorphic continuation.
result Bounded holomorphic functions on C(S,X)C(S,X) can be extended to holomorphic functions on B(S,X)B(S,X).

We give a new proof of the Morse Homology Theorem by constructing a chain complex associated to a Morse-Bott-Smale function that reduces to the Morse-Smale-Witten chain complex when the function is Morse-Smale and to the chain complex of smooth singular NN-cube chains when the function is constant. We show that the ho…

2006-12-12abs ↗pdf ↗

It is well-known that non-constant holomorphic functions do not exist on a compact complex manifold. This statement is false for a supermanifold with a compact reduction. In this paper we study the question under what conditions non-constant holomorphic functions do not exist on a compact homogeneous complex supermanif…

2010-07-09abs ↗pdf ↗

In this paper we construct Mabuchi LωM\mathcal{L}^{\rm M}_ω functional and Aubin-Yau functionals IωAY,JωAY\mathcal{I}^{\rm AY}_ω, \mathcal{J}^{\rm AY}_ω on any compact complex three-folds. The method presented here will be used in the forthcoming paper \cite{L1} on the construction of those functionals on any compact complex m…

2010-03-27abs ↗pdf ↗

The paper introduces new functionals and equations for complex vector bundles.

problem Complex vector bundle equations and their solutions.
method Introducing generalized Donaldson's functionals and discussing their Euler-Lagrange equations.
result Uniqueness of solutions to complex vector bundle equations.

We show how the space of complex spin structures of a closed oriented three-manifold embeds naturally into a space of quadratic functions associated to its linking pairing. Besides, we extend the Goussarov-Habiro theory of finite type invariants to the realm of compact oriented three-manifolds equipped with a complex s…

2002-07-22abs ↗pdf ↗

The SL(2)-character variety X of a closed surface M enjoys a natural complex-symplectic structure invariant under the mapping class group G of M. Using the ergodicity of G on the SU(2)-character variety, we deduce that every G-invariant meromorphic function on X is constant. The trace functions of closed curves on M de…

2003-04-21abs ↗pdf ↗

Complex-valued neural networks can approximate any continuous function.

problem Generalizing the universal approximation theorem to complex-valued networks.
method Characterizing activation functions for complex networks to approximate any continuous function.
result Different activation functions are required for deep vs shallow complex networks to achieve universal approximation.

Improved generalization bounds for CNNs using Rademacher complexity.

problem Establishing non-vacuous generalization bounds for deep learning models.
method Rademacher complexity framework with novel contraction lemmas for high-dimensional mappings.
result Enhanced generalization bounds for a broader class of activation functions.

Complex valued analytic torsion and dynamical zeta function studied on locally symmetric spaces.

problem Analyzing the Ruelle dynamical zeta function on locally symmetric spaces with flat vector bundles.
method Meromorphic extension and regularisation of the dynamical zeta function, relating it to the complex valued analytic torsion.
result The leading term of the dynamical zeta function at zero is related to the regularised determinant of the flat Laplacian.

SGD learns neural networks with a complexity measure called leap.

problem Time complexity of SGD learning on neural networks.
method Introduced a complexity measure called leap, proved conjecture for Gaussian data, and showed saddle-to-saddle dynamics.
result Proved a conjecture about the time complexity of learning functions with low-dimensional support.

New bounds on ReLU networks for low-regular functions.

problem Bounding approximation error for ReLU networks on low-regular functions.
method Complexity analysis of Fourier features residual networks to ReLU networks.
result Approximation error bound proportional to target function norm and inversely proportional to network width and depth.

We provide tight upper and lower bounds on the complexity of minimizing the average of mm convex functions using gradient and prox oracles of the component functions. We show a significant gap between the complexity of deterministic vs randomized optimization. For smooth functions, we show that accelerated gradient de…

2016-05-25abs ↗pdf ↗

Study on residual Monge-Ampère mass of complex functions with directional Lipschitz continuity.

problem Estimating the residual Monge-Ampère mass of plurisubharmonic functions.
method General decomposition formula under Sasakian structure, L1L^{1}-apriori estimate, upper-bound estimate on residual mass.
result Upper-bound estimate on residual mass for uniformly directional Lipschitz continuity confirmed.

New complexity measure explains neural network generalization gap.

problem Understanding the generalization gap between neural networks and linear models.
method Introducing a new complexity measure for functions that governs PAC-Bayes bounds and relates to neural network complexity.
result Demonstrates a separation in sample complexity between 2 and 4-layer neural networks for periodic functions.

Braids can be represented geometrically as curve diagrams. The geometric complexity of a braid is the minimal complexity of a curve diagram representing it. We introduce and study the corresponding notion of geometric generating function. We compute explicitly the geometric generating function for the group of braids o…

2015-03-02abs ↗pdf ↗

The paper uses MDM theory to analyze multifiltering functions on simplicial complexes.

problem Understanding multifiltering functions through discrete Morse theory.
method Applying multiparameter discrete Morse theory to vector-valued multifiltering functions.
result Any multifiltering function can be approximated by a compatible MDM function.

The paper analyzes risk bounds and Rademacher complexity in batch RL.

problem Estimating/minimizing Bellman error with general value function approximation.
method Characterizes generalization performance using Rademacher complexities of function classes.
result Risk bounds and Rademacher complexities provide insights into batch RL.

Introduces HTV to measure function complexity in learning schemes.

problem Assessing the complexity of supervised-learning schemes.
method Defines Hessian-Schatten total variation (HTV) as a seminorm to quantify function complexity.
result HTV is invariant to rotations, scalings, and translations, and its minimum value is achieved for linear mappings.

Transformer architecture struggles with complex tasks due to limitations in function composition.

problem Transformer architecture's limitations in handling complex tasks.
method Used Communication Complexity to prove limitations in composing functions.
result Transformer layer is incapable of handling large domain functions, even when domains are small.