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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.

168,742 papers · 148 categories

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48 results for concave elliptic Hessian operators

Study estimates eigenvalues for concave Hessian operators on convex domains.

problem Estimating eigenvalues for concave elliptic Hessian operators.
method Investigates Dirichlet eigenvalue problem for a broad class of concave elliptic Hessian operators.
result Existence and properties of the first nonzero eigenvalue and eigenfunction.

Study proves estimate for Hessian quotient equations on 2D Riemannian manifolds.

problem Problems posed by Delanoë and Urbas related to Hessian quotient equations.
method Maximum principle argument and new test function introduced to prove estimate.
result Unobstructed second order a priori estimate for real Hessian quotient equation in 2D.

Study concavity of solutions to elliptic equations under conformal deformations.

problem Establish concavity estimates for the principle eigenfunction of weighted Schrödinger operators.
method Analyzing the Dirichlet problem for the weighted Schrödinger operator \[-Δu + Vu = λρu\] with conformal connections.
result Partial resolution of Nguyen's conjecture on fundamental gap of horoconvex domains and power convexity estimate for solutions in spherical geometry.

Establishes log-concavity estimates for convex domains' first Dirichlet eigenfunctions.

problem Quantifying the Hessian of log-concave eigenfunctions on convex domains.
method Analyzes log-concavity properties of the first Dirichlet eigenfunction on convex domains.
result Obtains quantitative estimates for the Hessian of logu\log u.

Established concavity principle for curved spaces.

problem Solving equations on curved spaces with nonnegative curvature.
method Applied concavity principle to elliptic and parabolic equations on locally symmetric spaces with nonnegative curvature.
result First general concavity principle on spaces with non-constant sectional curvature.

This study solves a Dirichlet problem for specific elliptic equations on Riemannian manifolds with concave boundaries.

problem Solving the Dirichlet problem for degenerate elliptic equations on Riemannian manifolds with mean concave boundaries.
method The proof relies on a quantitative boundary estimate.
result Analogous results are obtained in complex variables and on certain product manifolds.

We study Hessian fully nonlinear uniformly elliptic equations and show that the second derivatives of viscosity solutions of those equations (in 12 or more dimensions) can blow up in an interior point of the domain. We prove that the optimal interior regularity of such solutions is no more than C^{1+ε}, showing the opt…

2008-05-17abs ↗pdf ↗

Solves curvature problems on manifolds with negative curvature.

problem Prescribed curvature problems on closed manifolds with negative curvature.
method Investigates fully nonlinear prescribed curvature problems for modified Schouten tensor on closed Riemannian manifolds with negative curvature.
result Proves solvability of curvature problems under certain conditions.

Study elliptic equations on hyperhermitian manifolds with flat hyperkähler metric.

problem Solving elliptic equations on compact hyperhermitian manifolds.
method Adapting Székelyhidi's approach to the hypercomplex setting.
result Prove a priori estimates for solutions to elliptic equations.

Let uu denote a solution to a rotationally invariant Hessian equation F(D2u)=0F(D^2u)=0 on a bounded simply connected domain ΩR2Ω\subset R^2, with constant Dirichlet and Neumann data on Ω\partial Ω. In this paper we prove that if uu is real analytic and not identically zero, then uu is radial and ΩΩ is a disk. The fully …

2019-02-05abs ↗pdf ↗

Study fully nonlinear elliptic equations on compact hyperhermitian manifolds.

problem Solving fully nonlinear elliptic equations on compact hyperhermitian manifolds.
method Adapting Székelyhidi's approach to the hypercomplex setting, proving a priori estimates.
result Proves solvability of quaternionic Hessian and Monge-Ampère equations on compact flat hyperkähler manifolds.

MALA mixes efficiently under smoothness and isoperimetry assumptions.

problem Sampling from target densities efficiently.
method Metropolis-Adjusted Langevin algorithm (MALA) with smoothness and isoperimetry assumptions.
result MALA mixes in $O\left(\frac{(LΥ)^{\frac12}}{ψ_μ^2} \log\left(\frac{1}ε ight) ight)$ iterations.

Let L be an ample bundle over a compact complex manifold X. Fix a Hermitian metric in L whose curvature defines a Kähler metric on X. The Hessian of Mabuchi energy is a fourth-order elliptic operator D on functions which arises in the study of scalar curvature. We quantise D by the Hessian E(k) of balancing energy, a f…

2010-09-23abs ↗pdf ↗

This research accelerates sampling methods using Nesterov's Acceleration.

problem Improving sampling efficiency in MCMC methods.
method Developed a Hessian-Free High-Resolution ODE reformulation of NAG-SC, injected noise, and discretized the diffusion process.
result Quantified acceleration beyond underdamped Langevin in W2W_2 distance for log-strongly-concave targets.

We present a method to derive local estimates for some classes of fully nonlinear elliptic equations. The advantage of our method is that we derive Hessian estimates directly from C0C^0 estimates. Also, the method is flexible and can be applied to a large class of equations.

2005-10-29abs ↗pdf ↗

We derive a priori estimates for solutions of a general class of fully non-linear equations on compact Hermitian manifolds. Our method is based on ideas that have been used for different specific equations, such as the complex Monge-Ampère, Hessian and inverse Hessian equations. As an application we solve a class of He…

2015-01-12abs ↗pdf ↗

Optimistic method adapted for faster convex-concave min-max problems.

problem Solving convex-concave min-max optimization problems efficiently.
method Adaptive, line search-free second-order methods combining optimistic updates and second-order information.
result Achieves optimal convergence rate without line search or backtracking.

BBVI converges nearly dimensionally independent for log-concave targets.

problem Efficiently optimizing variational parameters in high-dimensional spaces.
method Proved convergence rate of BBVI with reparametrization gradient for log-concave targets.
result BBVI converges with nearly independent dimension dependence for log-concave targets.

Study on eigenvalues of complex Hessian operator on pseudoconvex manifolds.

problem Eigenvalue problem for complex Hessian operator on pseudoconvex manifolds.
method Established C1,1C^{1,1}-regularity and uniqueness of the first eigenfunction, derived variational formula for the first eigenvalue.
result Derivation of a bifurcation-type theorem and geometric bounds for the eigenvalue.

This study explores the index theory of Heisenberg elliptic and transversally Heisenberg elliptic operators using KKKK-theory.

problem Analyzing the index theory of Heisenberg elliptic and transversally Heisenberg elliptic operators.
method Applying Kasparov's methodology and examining specific conditions using Fourier transform of the nilpotent group CC^*-algebra.
result Demonstrated enhanced methods for analyzing hypoellipticity and defined transversal Heisenberg ellipticity in a KKKK-theoretic context.

Paper discusses solving generalized Hessian inequalities with various operators.

problem Finding global solutions to generalized Hessian inequalities.
method Analyzes various Hessian operators and provides conditions for global solvability.
result Provides necessary and sufficient conditions for global solvability of generalized Hessian inequalities.

Mathai, Melrose, and Singer introduced the notion of projective elliptic operators on manifolds equipped with an Azumaya bundle. In this note we compute the equivariant index of transversally elliptic operators that are the pullback of projective elliptic operators on the trivialization of the Azumaya bundle. It encomp…

2016-10-18abs ↗pdf ↗

New algorithms solve complex minimax problems efficiently.

problem Nonconvex-strongly concave minimax problems in machine learning.
method Gradient norm regularized trust-region (GRTR) and Levenberg-Marquardt (LMNegCur) algorithms.
result Proved iteration complexities matching best known results.