Conveners
Poster Session: Poster Session
- Davide Palitta (Alma Mater Studiorum, Università di Bologna)
Multilayer networks are often used to model systems where entities are connected in multiple ways simultaneously, capturing the complexity of real-world relationships better than traditional single-layer networks [1]. The dynamical evolution of a complex system over time can be described through a particular interlayer structure. The extent to which properties of evolving networks can be...
The Hadamard factorization is a powerful technique for data analysis and matrix compression, which decomposes a given matrix $A$ into the element-wise product of two low-rank matrices $W$ and $H$ such that $A\approx W\circ H$. Unlike the well-known SVD, this decomposition allows to represent higher-rank matrices with the same amount of variables. We present some new theoretical results which...
We consider a class of differential problems set in a Banach space, with integral boundary conditions:
\begin{equation}
\frac{dv}{dt} = Av, \qquad 0<t<T,\qquad \frac{1}{T}\int_0^T v(t) dt = f,
\end{equation}
where $A$ is a linear, closed, possibly unbounded operator (e.g., second derivative in space). Note that the finite-dimensional version of this problem, where $A$ is a matrix, is...
Given a matrix $A\in \mathbb R^{s\times s}$ and a vector $\mathbf {f} \in \mathbb{ R } ^s,$ under mild assumptions the non-local boundary value problem
\begin{eqnarray}
&&\odv{\mathbf{u}}{\tau} = A \mathbf{u}, \quad 0<\tau<1, \label{l1} \
&&\displaystyle \int_0^1 \mathbf{u}(\tau) \,\mathrm{d}\tau = \mathbf {f}, \label{l2}
\end{eqnarray}
admits as unique solution
[
...
Quantum block encoding (QBE) is a crucial step in the development of many quantum algorithms, as it embeds a given matrix into a suitable larger unitary matrix. Historically, efficient techniques for QBE have primarily focused on sparse matrices, with less attention given to data-sparse matrices, such as rank-structured matrices. In this work, we examine a specific case of rank structure:...
Given two matrices $X,Y\in\mathbb{R}^{n\times m}$ with $m
The randomized singular value decomposition (SVD) proposed in [1] has become one of the most well-established randomization-based algorithms in numerical linear algebra. Its core idea is the computation of a subspace that approximates the column space of a target matrix $\mathbf{A}$ with high probabilistic confidence. In our work [2], we introduce a modification to the standard randomized SVD...
Implicit Runge-Kutta (IRK) methods [2] are highly effective for solving stiff ordinary differential equations (ODEs)
\begin{array}{ll}
M {y}'(t)=f({y}(t),t), & t \in [0,T], \
{y}(0) = {y}_0.
\end{array}
However, their usage can be computationally expensive for large-scale problems due to the need to solve coupled algebraic equations at each step. This study improves IRK...
