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Two $k$-ary Fibonacci recurrences are $a_k(n) = a_k(n-1) + k \cdot a_k(n-2)$ and $b_k(n) = k \cdot b_k(n-1) + b_k(n-2)$. We provide a simple proof that $a_k(n)$ is the number of $k$-regular words over $[n] = \{1,2,\ldots,n\}$ that avoid patterns $\{121, 123, 132, 213\}$ when using base cases $a_k(0) = a_k(1) = 1$ for any $k \geq 1$. This was previously proven by Kuba and Panholzer in the context of Wilf-equivalence for restricted Stirling permutations, and it creates Simion and Schmidt's classic result on the Fibonacci sequence when $k=1$, and the Jacobsthal sequence when $k=2$. We complement this theorem by proving that $b_k(n)$ is the number of $k$-regular words over $[n]$ that avoid $\{122, 213\}$ with $b_k(0) = b_k(1) = 1$ for any~$k \geq 2$. Finally, we conjecture that $|Av^{2}_{n}(\underline{121}, 123, 132, 213)| = a_1(n)^2$ for $n \geq 0$. That is, vincularizing the Stirling pattern in Kuba and Panholzer's Jacobsthal result gives the Fibonacci-squared numbers.

Given a set $S$ of $n$ points in the plane and a parameter $\varepsilon>0$, a Euclidean $(1+\varepsilon)$-spanner is a geometric graph $G=(S,E)$ that contains, for all $p,q\in S$, a $pq$-path of weight at most $(1+\varepsilon)\|pq\|$. We show that the minimum weight of a Euclidean $(1+\varepsilon)$-spanner for $n$ points in the unit square $[0,1]^2$ is $O(\varepsilon^{-3/2}\,\sqrt{n})$, and this bound is the best possible. The upper bound is based on a new spanner algorithm in the plane. It improves upon the baseline $O(\varepsilon^{-2}\sqrt{n})$, obtained by combining a tight bound for the weight of a Euclidean minimum spanning tree (MST) on $n$ points in $[0,1]^2$, and a tight bound for the lightness of Euclidean $(1+\varepsilon)$-spanners, which is the ratio of the spanner weight to the weight of the MST. Our result generalizes to Euclidean $d$-space for every constant dimension $d\in \mathbb{N}$: The minimum weight of a Euclidean $(1+\varepsilon)$-spanner for $n$ points in the unit cube $[0,1]^d$ is $O_d(\varepsilon^{(1-d^2)/d}n^{(d-1)/d})$, and this bound is the best possible. For the $n\times n$ section of the integer lattice in the plane, we show that the minimum weight of a Euclidean $(1+\varepsilon)$-spanner is between $\Omega(\varepsilon^{-3/4}\cdot n^2)$ and $O(\varepsilon^{-1}\log(\varepsilon^{-1})\cdot n^2)$. These bounds become $\Omega(\varepsilon^{-3/4}\cdot \sqrt{n})$ and $O(\varepsilon^{-1}\log(\varepsilon^{-1})\cdot \sqrt{n})$ when scaled to a grid of $n$ points in the unit square. In particular, this shows that the integer grid is \emph{not} an extremal configuration for minimum weight Euclidean $(1+\varepsilon)$-spanners.

In this paper we study two dimensional minimal linear code over the ring $\mathbb{Z}_{p^n}$(where $p$ is prime). We show that if the generator matrix $G$ of the two dimensional linear code $M$ contains $p^n+p^{n-1}$ column vector of the following type {\scriptsize{$u_{l_1}\begin{pmatrix} 1\\ 0 \end{pmatrix}$, $u_{l_2}\begin{pmatrix} 0\\1 \end{pmatrix}$, $u_{l_3}\begin{pmatrix} 1\\u_1 \end{pmatrix}$, $u_{l_4}\begin{pmatrix} 1\\u_2 \end{pmatrix}$, \ldots,$u_{l_{p^n-p^{n-1}+2}} \begin{pmatrix} 1\\u_{p^n-p^{n-1}} \end{pmatrix}$, $u_{l_{p^n-p^{n-1}+3}}\begin{pmatrix} d_1 \\ 1 \end{pmatrix}$, $u_{l_{p^n-p^{n-1}+4}}\begin{pmatrix} d_2\\ 1 \end{pmatrix}$,\ldots \dots,\\ $u_{l_{p^n+1}}\begin{pmatrix} d_{p^{n-1}-1}\\1 \end{pmatrix}$, $u_{l_{p^n+2}}\begin{pmatrix} 1\\d_1 \end{pmatrix}$, $u_{l_{p^n+3}}\begin{pmatrix} 1\\d_2 \end{pmatrix}$,\ldots,$u_{l_{p^n+p^{n-1}}}\begin{pmatrix} 1 \\d_{p^{n-1}-1} \end{pmatrix}$}}, where $u_i$ and $d_j$ are distinct units and zero divisors respectively in the ring $\mathbb{Z}_{p^n}$ for $1\leq i \leq p^n+p^{n-1}$, $1\leq j \leq p^{n-1}-1$ and additionally, denote $u_{l_i}$ as units in $\mathbb{Z}_{p^n}$, then the module generated by $G$ is a minimal linear code. Also we show that if any one column vector of the above types are not present entirely in $G$, then the generated module is not a minimal linear code.

This work delves into the exponential time differencing (ETD) schemes for the matrix-valued Allen-Cahn equation. In fact, the maximum bound principle (MBP) for the first- and second-order ETD schemes is presented in a prior publication [SIAM Review, 63(2), 2021], assuming a symmetric initial matrix field. Noteworthy is our novel contribution, demonstrating that the first- and second-order ETD schemes for the matrix-valued Allen-Cahn equation -- both being linear schemes -- unconditionally preserve the MBP, even in instances of nonsymmetric initial conditions. Additionally, we prove that these two ETD schemes preserve the energy dissipation law unconditionally for the matrix-valued Allen-Cahn equation. Some numerical examples are presented to verify our theoretical results and to simulate the evolution of corresponding matrix fields.

An initial-boundary value problem with a Caputo time derivative of fractional order $\alpha\in(0,1)$ is considered, solutions of which typically exhibit a singular behaviour at an initial time. For this problem, we give a simple and general numerical-stability analysis using barrier functions, which yields sharp pointwise-in-time error bounds on quasi-graded temporal meshes with arbitrary degree of grading. L1-type and Alikhanov-type discretization in time are considered. In particular, those results imply that milder (compared to the optimal) grading yields optimal convergence rates in positive time. Semi-discretizations in time and full discretizations are addressed. The theoretical findings are illustrated by numerical experiments.

Given a matrix-valued function $\mathcal{F}(\lambda)=\sum_{i=1}^d f_i(\lambda) A_i$, with complex matrices $A_i$ and $f_i(\lambda)$ entire functions for $i=1,\ldots,d$, we discuss a method for the numerical approximation of the distance to singularity of $\mathcal{F}(\lambda)$. The closest singular matrix-valued function $\widetilde{\mathcal{F}}(\lambda)$ with respect to the Frobenius norm is approximated using an iterative method. The property of singularity on the matrix-valued function is translated into a numerical constraint for a suitable minimization problem. Unlike the case of matrix polynomials, in the general setting of matrix-valued functions the main issue is that the function $\det ( \widetilde{\mathcal{F}}(\lambda) )$ may have an infinite number of roots. An important feature of the numerical method consists in the possibility of addressing different structures, such as sparsity patterns induced by the matrix coefficients, in which case the search of the closest singular function is restricted to the class of functions preserving the structure of the matrices.

Analysis-suitable $G^1$ (AS-$G^1$) multi-patch spline surfaces [4] are particular $G^1$-smooth multi-patch spline surfaces, which are needed to ensure the construction of $C^1$-smooth multi-patch spline spaces with optimal polynomial reproduction properties [16]. We present a novel local approach for the design of AS-$G^1$ multi-patch spline surfaces, which is based on the use of Lagrange multipliers. The presented method is simple and generates an AS-$G^1$ multi-patch spline surface by approximating a given $G^1$-smooth but non-AS-$G^1$ multi-patch surface. Several numerical examples demonstrate the potential of the proposed technique for the construction of AS-$G^1$ multi-patch spline surfaces and show that these surfaces are especially suited for applications in isogeometric analysis by solving the biharmonic problem, a particular fourth order partial differential equation, with optimal rates of convergence over them.

We revisit $k$-Dominating Set, one of the first problems for which a tight $n^k-o(1)$ conditional lower bound (for $k\ge 3$), based on SETH, was shown (P\u{a}tra\c{s}cu and Williams, SODA 2007). However, the underlying reduction creates dense graphs, raising the question: how much does the sparsity of the graph affect its fine-grained complexity? We first settle the fine-grained complexity of $k$-Dominating Set in terms of both the number of nodes $n$ and number of edges $m$. Specifically, we show an $mn^{k-2-o(1)}$ lower bound based on SETH, for any dependence of $m$ on $n$. This is complemented by an $mn^{k-2+o(1)}$-time algorithm for all $k\ge 3$. For the $k=2$ case, we give a randomized algorithm that employs a Bloom-filter inspired hashing to improve the state of the art of $n^{\omega+o(1)}$ to $m^{\omega/2+o(1)}$. If $\omega=2$, this yields a conditionally tight bound for all $k\ge 2$. To study if $k$-Dominating Set is special in its sensitivity to sparsity, we consider a class of very related problems. The $k$-Dominating Set problem belongs to a type of first-order definable graph properties that we call monochromatic basic problems. These problems are the natural monochromatic variants of the basic problems that were proven complete for the class FOP of first-order definable properties (Gao, Impagliazzo, Kolokolova, and Williams, TALG 2019). We show that among these problems, $k$-Dominating Set is the only one whose fine-grained complexity decreases in sparse graphs. Only for the special case of reflexive properties, is there an additional basic problem that can be solved faster than $n^{k\pm o(1)}$ on sparse graphs. For the natural variant of distance-$r$ $k$-dominating set, we obtain a hardness of $n^{k-o(1)}$ under SETH for every $r\ge 2$ already on sparse graphs, which is tight for sufficiently large $k$.

The LASSO is a recent technique for variable selection in the regression model \bean y & = & X\beta + z, \eean where $X\in \R^{n\times p}$ and $z$ is a centered gaussian i.i.d. noise vector $\mathcal N(0,\sigma^2I)$. The LASSO has been proved to achieve remarkable properties such as exact support recovery of sparse vectors when the columns are sufficently incoherent and low prediction error under even less stringent conditions. However, many matrices do not satisfy small coherence in practical applications and the LASSO estimator may thus suffer from what is known as the slow rate regime. The goal of the present paper is to study the LASSO from a slightly different perspective by proposing a mixture model for the design matrix which is able to capture in a natural way the potentially clustered nature of the columns in many practical situations. In this model, the columns of the design matrix are drawn from a Gaussian mixture model. Instead of requiring incoherence for the design matrix $X$, we only require incoherence of the much smaller matrix of the mixture's centers. Our main result states that $X\beta$ can be estimated with the same precision as for incoherent designs except for a correction term depending on the maximal variance in the mixture model.

Given a Binary Decision Diagram $B$ of a Boolean function $\varphi$ in $n$ variables, it is well known that all $\varphi$-models can be enumerated in output polynomial time, and in a compressed way (using don't-care symbols). We show that all $N$ many $\varphi$-models of fixed Hamming-weight $k$ can be enumerated as well in time polynomial in $n$ and $|B|$ and $N$. Furthermore, using novel wildcards, again enables a compressed enumeration of these models.