arXiv subjects

Alejandro Mahillo

Publications and source records attributed to Alejandro Mahillo.

4 recordsLinked to original sources

arXiv2026

Ces\`aro-Hardy operators on $L^p[0,1]$: fine spectrum, weighted Koopman semigroups and invariant subspaces

In this paper we study boundedness and detailed spectral properties for the Ces\`aro-Hardy operator and some generalizations in $L^p[0,1]$. The study employs $C_0$-semigroup theory, expressing the Ces\`aro-Hardy operators and their dual operators through subordination with $C_0$-semigroups $T(t)$ and $S(t)$ respectively. The spectral properties of the semigroup's infinitesimal generators are transferred to the Ces\`aro-Hardy operators using functional calculus methods. Furthermore, some implications for the Invariant Subspace Problem are explored by demonstrating the universality of certain translations related to the semigroup $T(t)$, and providing results on the invariant subspaces of these operators.

math.FA↗

arXiv2024

A Ritt-Kreiss condition: spectral localization and norm estimates

A new condition is introduced by generalizing the Ritt and Kreiss operators named $(\alpha, \beta)$-RK condition. Geometrical properties of the spectrum for the case $\beta < 1$ are studied, moreover it is shown that in that case if $\alpha + \beta = 1$ the operator is Ritt. Estimates for the power and power differences norms for this type of operators are also studied. Lastly we apply this theory to obtain and interpolation result over Ritt and Kreiss operator on $L^p$ spaces.

math.FA↗

arXiv2024

Discrete Besov spaces via semigroups associated to the discrete Laplacian and regularity of non-local operators

In this paper we prove characterizations of the discrete Besov spaces in terms of the heat and Poisson semigroups associated with the discrete Laplacian that will allow us to prove regularity results for the fractional powers of the discrete Laplacian and the discrete Bessel potentials. Moreover, we provide new estimates for the derivatives of the discrete heat kernel and semigroup which are of independent interest.

math.CA↗

arXiv2021

A Discrete Variational Derivation of Accelerated Methods in Optimization

Many of the new developments in machine learning are connected with gradient-based optimization methods. Recently, these methods have been studied using a variational perspective. This has opened up the possibility of introducing variational and symplectic methods using geometric integration. In particular, in this paper, we introduce variational integrators which allow us to derive different methods for optimization. Using both, Hamilton's and Lagrange-d'Alembert's principle, we derive two families of respective optimization methods in one-to-one correspondence that generalize Polyak's heavy ball and the well known Nesterov accelerated gradient method, the second of which mimics the behavior of the first reducing the oscillations of classical momentum methods. However, since the systems considered are explicitly time-dependent, the preservation of symplecticity of autonomous systems occurs here solely on the fibers. Several experiments exemplify the result.

math.OC↗