Remarks on constructing biharmonic and conformal biharmonic maps to spheres

This paper investigates a geometric algorithm for constructing biharmonic and conformal-biharmonic maps into spheres, demonstrating that while the approach faces strong restrictions for biharmonic maps on closed domains due to the maximum principle, it offers greater flexibility on non-compact domains and successfully generates explicit critical points for conformal-biharmonic maps between spheres.

Volker Branding2026-03-09🔢 math

The level of self-organized criticality in oscillating Brownian motion: nn-consistency and stable Poisson-type convergence of the MLE

This paper establishes that for discretely observed oscillating Brownian motion, the maximum likelihood estimator of the self-organized criticality level achieves nn-consistency and converges stably to a bivariate Poisson-type distribution, despite the non-standard challenge posed by the discontinuity of the transition density at the true parameter.

Johannes Brutsche, Angelika Rohde2026-03-09🔢 math

Dynamically optimal portfolios for monotone mean--variance preferences

This paper provides the first complete characterization of optimal dynamic portfolio choice for monotone mean-variance utility in asset models with independent returns under minimal assumptions, establishing a link between maximal utility and the monotone Sharpe ratio while deriving conditions under which classical mean-variance efficient portfolios remain optimal.

Aleš Černý, Johannes Ruf, Martin Schweizer2026-03-09🔢 math

Metric Entropy of Ellipsoids in Banach Spaces: Techniques and Precise Asymptotics

This paper introduces new techniques to provide a unified framework for computing the metric entropy of ellipsoids in Banach spaces, delivering precise asymptotic expansions with explicit constants, improved second-order terms, and the first exact characterization for infinite-dimensional bodies, with significant applications to function classes in Sobolev and Besov spaces and machine learning.

Thomas Allard, Helmut Bölcskei2026-03-09🔢 math

Quantization of Probability Distributions via Divide-and-Conquer: Convergence and Error Propagation under Distributional Arithmetic Operations

This paper introduces and analyzes a divide-and-conquer algorithm for quantizing one-dimensional probability distributions, establishing a universal Wasserstein-1 error bound and demonstrating through numerical experiments that the method achieves optimal convergence rates while offering superior stability under arithmetic operations compared to existing schemes.

Bilgesu Arif Bilgin, Olof Hallqvist Elias, Michael Selby, Phillip Stanley-Marbell2026-03-09🔢 math