Papers

Stability of switched linear systems under arbitrary switching is a fundamental problem in control theory, closely related to the joint spectral radius (JSR), which characterizes the worst-case growth rate of system trajectories. In this paper, we contribute to the path-complete approach for approximating the JSR. This framework constructs algebraic stability certificates using labeled directed graphs, known as path-complete graphs. These certificates can be computed via an associated optimization problem. We propose an iterative algorithm that refines path-complete graphs in an efficient and parsimonious manner. The algorithm relies on a graph-theoretic analysis of the optimality conditions of the underlying optimization problem. In particular, we derive a sufficient condition under which the exact JSR is attained by a given path-complete graph. When this condition is not satisfied, we identify bottleneck nodes by analyzing the graph induced by the active constraints. We then use this information to refine the path-complete graph via local graph lifting (node splitting), and repeat the procedure. Numerical experiments demonstrate the effectiveness and scalability of the proposed approach, outperforming state-of-the-art methods on all challenging instances tested.

In characteristic zero, the category of strict polynomial functors is well-known to be the tensor abelian category freely generated by one object. We show that this property fails in positive characteristic, but that it can be repaired by restricting the class of tensor abelian categories considered. The new universal property recovers several known constructions and shows that Ext-algebras of strict polynomial functors act on cohomological computations in many other contexts.

High-order Hermite semi-Lagrangian schemes on unstructured triangular grids are proposed for advection equations, based on Bell and Argyris finite elements. Nodal semi-Lagrangian schemes transport point values together with gradients and Hessians, while projection semi-Lagrangian schemes are basically semi-Lagrangian Discontinuous Galerkin schemes (SLDG) with straight remaining backward triangles and shared degrees of freedoms. Stability and convergence studies of the different schemes are carried out numerically.

We consider weak solutions to $p$-Laplace equations in cylindrical domains under mixed homogeneous Dirichlet-Neumann boundary conditions. We assume that the right-hand side is positive and locally Lipschitz continuous and we prove that any positive solution is monotone increasing in the $x_N$ direction for any $p>1$. As an application we prove that solutions to Allen-Cahn type equations are one-dimensional as well as a Liouville type result for Lane-Emden type equations.

In this work, we introduce a multi-objective version of the well-known single-row facility layout problem (SRFLP). In the SRFLP, a set of one-dimensional facilities should be placed along a single line such that the weighted sum of the center-to-center distances of each pair of facilities is minimized. In our multi-objective extension, there are multiple such weighted-sum objectives which we consider under the concept of Pareto optimality. We develop a solution algorithm based on the $\varepsilon$-constraint method to solve the bi-objective SRFLP. Many existing works on the $\varepsilon$-constraint method use integer linear programming (ILP) solvers in a black-box fashion for solving the problems at the individual iterations of the method. In contrast to that, we use our own branch-and-bound procedure based on semidefinite programming (SDP), as SDP relaxations are known to be more effective for solving the SRFLP than linear programming relaxations of ILPs. This allows us to propose several enhancements procedures for our $\varepsilon$-constraint approach, such as non-binary branching and reusing of nodes within the branch-and-bound trees, which are usually not possible when using black-box solvers. We present a computational study to demonstrate the effectiveness of our solution approach and its enhancements.

A connected graph is matching covered if it has at least one edge and every edge lies in some perfect matching.Lovász proved that every matching covered graph G can be uniquely decomposed into a list of bricks and braces up to multiple edges. Denote by b(G) the number of bricks in such a decomposition. An edge e of G is removable if G-e is also matching covered; is b-invariant if e is removable and b(G-e)=b(G). Furthermore, an edge e of G is a forcing edge if it lies in precisely one perfect matching of G. Lucchesi and Murty proposed the problem of characterizing bricks, distinct from K_4, \overline{C_6}, and the Petersen graph, in which every b-invariant edge is a forcing edge. In this paper, we solve this problem for near-bipartite bricks by providing a complete characterization.

The vehicle routing problem (VRP) is a central optimization problem in artificial intelligence, logistics automation, transportation scheduling, and industrial decision-making. VRP and its variants are NP-hard, and practical routing tasks often combine time windows, vehicle capacities, pickup-and-delivery relations, dynamic requests, and other operational constraints, making both modeling and solving difficult. Large language models (LLMs) provide a flexible interface for routing optimization by processing natural-language requirements, generating code, reasoning over constraints, and interacting with external tools. This survey reviews LLM-driven research on VRP, covering the basic definition, main variants, major solver families, and LLM concepts needed for this topic. Existing studies are organized into three roles: modelers translate natural-language requirements into constraints and modeling code; designers generate heuristics, operators, or route plans; and coordinators organize tool calls, multi-agent collaboration, and connections with neural solvers. The survey also reviews standard benchmarks, real or near-real operational datasets, LLM-oriented evaluation frameworks, and two comparative experiments. The goal is to clarify current progress in LLM-assisted routing optimization and provide a structured reference for intelligent decision-making, advanced manufacturing, and industrial automation.

Off-shell Bethe vectors for a generic $\fg$ invariant integrable model are constructed through the currents of the Yangian doubles of the classical series. These off-shell Bethe vectors are shown to satisfy the defining properties which were used in \cite{LPR-RR} to prove the rectangular recurrence relations and verify the eigenvalue property of the on-shell Bethe vectors in $\ggo$-invariant integrable models.

Let $\Psi (x,y)$ denote the count of $y$-smooth numbers below $x$ and $P(n)$ denote the largest prime factor of $n$. We show that \[ \frac{1}{\varphi(q)} \sum_{\chi \bmod q} \Bigl| \sum_{\substack{n \leq x \\ P(n) \leq y}} \chi(n) \Bigr| = o \Bigl( \sqrt{\Psi(x,y)} \Bigr), \] whenever $(\log x)^6 \leq y \leq x^{\frac{1}{32 \log \log x}}$ and $q \geq x^{1 + \varepsilon}$ for some small quantifiable $\varepsilon > 0$. The saving is substantial when $\varepsilon$ is fixed away from zero, and we prove similar results for continuous characters and completely multiplicative twists of these sums.

We investigate statistical properties of certain stationary point processes, namely determinantal processes with projection kernels and Gibbs point processes with superstable pair interactions. These are examples of hyperuniform and non-hyperuniform stationary point processes, respectively. We are interested in the variance of their second order statistics within a ball around the origin, and we study the asymptotic growth of this variance as the radius of the ball goes to infinity. It is shown that, generically, for both types of processes the variance is asymptotically proportional to the volume of the ball. In other words: the second order statistics of these point processes behave non-hyperuniform. For Gibbs processes with superstable interactions these results have an interesting application to the so-called inverse Henderson problem of statistical mechanics. We also show that the structure factor (respectively the Bartlett spectral measure) of these Gibbs processes is strictly positive, while it is positive except for a simple zero at the origin for the determinantal processes.

We present a simple, yet general approach to study the scaling properties as the dimensionality of Metropolised MCMC sampling algorithms increases. The study relies on the symmetries of the Hamiltonian formalism and ultimately on the symmetry of the Metropolis-Hastings formula. Our findings contain, as particular cases, many known results for the Random Walk Metropolis, MALA and other algorithms. In addition, they provide, in an easy way, new optimal scaling results for a variety of proposal mechanisms, including implicit proposals and proposals generated with the help of differential equation integrators. The analysis applies to targets that are products of a given, not necessarily univariate distribution, and also to cases where the different terms in the product are scaled differently. We show how to construct gradient-based MALA-like proposals where the variance of the proposal as the dimension $d$ increases may be taken as $O(1/d^\mu)$, with $\mu>0$ arbitrarily small, to be compared with the values $\mu = 1$ for Random Walk Metropolis and $\mu=1/3$ for MALA.

We study the HK conjecture and the gap-labelling problem for transformation groupoids associated with free actions of poly-$\Z$ groups on Cantor sets. The main tool is a comparison of the long exact sequences in groupoid homology and cohomology with the Pimsner–Voiculescu exact sequence for crossed products by $\Z$. In addition to the canonical homology comparison maps $\mu_0$ and $\mu_1$, we introduce cohomology comparison maps associated with suitable $K$-theory classes of the acting group. Together with Poincaré duality, these maps detect the higher homology terms occurring in the HK conjecture. We apply this method to free actions of poly-$\Z$ groups of small Hirsch length. For actions of $\Z$, $\Z^2$, and the Klein bottle group, we recover HK and gap-labelling. For several classes of groups of Hirsch length three and four, we either prove HK or obtain explicit exact sequences describing the $K$-groups in terms of groupoid homology and cohomology. For gap-labelling, we combine the de la Harpe–Skandalis determinant, the trace formula for the Pimsner–Voiculescu boundary map, and transposition decompositions in topological full groups. This gives gap-labelling up to a factor of two for all free actions of poly-$\Z$ groups of Hirsch length three and for certain groups of Hirsch length four, including $\Z^4$. We also recover gap-labelling for $\Z^3$-actions and prove gap-labelling up to a factor of two for $\Z^5$-actions by using cohomology comparison maps for mapping tori.

We prove that Lawson's planar closed-disk domain is not an RB-domain. This domain is the dcpo of all closed disks in the Euclidean plane, together with the whole plane as bottom, ordered by reverse inclusion. Since this domain is an FS-domain, it gives a concrete example of an FS-domain which is not an RB-domain, answering negatively the long-standing open problem in domain theory of whether FS-domains and RB-domains are identical.

We investigate the $L_p$ Brunn-Minkowski inequality for dual quermassintegrals in weighted measure spaces, which is a special class of rotationally invariant measures proposed by Cordero-Erausquin and Rotem [Ann. Probab., 51 (2023)]. Specifically, the weighted dual quermassintegral is defined by integrating the radial density $|x|^{q-n}\phi(|x|)$ for $q\in(0,n]$, where $\phi$ is a positive radially non-increasing weight, it recovers the classical dual quermassintegral when $\phi\equiv1$. For $p\geq1$, we prove the $L_p$ Brunn-Minkowski inequality with concavity exponent $p/q$ under the condition that $t\mapsto\log\phi(e^t)$ is concave, which is exactly the natural convexity condition from Cordero-Erausquin and Rotem's paper in general, improving the exponent $1/n$ when $p=1$. For $p\in(0,1)$, we obtain the result with exponent $p/q$ under more strictly weight assumptions, together with explicit lower bounds for the admissible range of $p$.

A graph is non-$r$-partite if its chromatic number exceeds $r$. For an edge-color-critical graph $F$ with $\chi(F)=r+1$, let $\mathrm{ex}_{r+1,\rho}(n,F)$ be the maximum adjacency spectral radius among non-$r$-partite $F$-free graphs of order $n$, and let $\mathrm{EX}_{r+1,\rho}(n,F)$ and $\mathrm{EX}_{r+1}(n,F)$ be the families of such graphs attaining, respectively, this maximum spectral radius and the maximum number of edges $\mathrm{ex}_{r+1}(n,F)$. Fang and Lin conjectured that $\mathrm{EX}_{r+1,\rho}(n,F)\subseteq\mathrm{EX}_{r+1}(n,F)$ for every such $F$ and all large $n$. We prove a reduction principle: if $F$ is \emph{$s$-embeddable} and $\mathrm{ex}_{r+1}(n,F)=|E(T_{n,r})|-\lfloor n/r\rfloor+2(s-1)$, where $T_{n,r}$ is the Turán graph, then the inclusion holds and, moreover, the spectral extremal graph is unique. The reduction replaces the spectral problem by an edge-counting one, and its proof rests on a direct comparison of secular functions together with a second-order residual refinement of the Rayleigh principle. We then determine the spectral extremal graphs for all edge-color-critical complete multipartite forbidden graphs. For $F=K_{1,1,t_3,\ldots,t_{r+1}}$ with $t_3,\ldots,t_{r+1}\ge 2$ we show \[ \mathrm{ex}_{r+1}(n,F)=|E(T_{n,r})|-\Bigl\lfloor\frac nr\Bigr\rfloor+2(t_{\min}-1), \qquad t_{\min}:=\min\{t_3,\ldots,t_{r+1}\}, \] for all sufficiently large $n$, and we identify the unique spectral extremal graph; in particular $\mathrm{EX}_{r+1,\rho}(n,F)\subseteq\mathrm{EX}_{r+1}(n,F)$. The endpoint $t_3=1$ lies outside the embeddability framework and is treated by a separate argument: for $F=K_{1,1,1,t_4,\ldots,t_{r+1}}$ with $r\ge3$, the unique non-$r$-partite spectral extremal graph is $Y_r(n)$, obtained through a saturation reduction followed by the spectral refinement of Turán's theorem. The complete graph $K_{r+1}$ and the complete split graph $B_{r,q}$

We propose a multilevel stochastic-gradient neural solver (MLSG) for second-kind boundary integral equations. MLSG represents the unknown boundary density using a neural network optimized by stochastic residual minimization over a hierarchy of successively refined Nyström discretizations. Upon transitioning from one level to the next, the network parameters obtained on the previous level initialize training on the current one. This coarse-to-fine strategy retains a continuous, grid-independent density representation and is designed to reduce the total computational effort required to reach a prescribed residual tolerance at the target resolution. The algorithm avoids grid-transfer operators and hierarchical fast-summation machinery, relying instead on batched kernel evaluations and standard network forward and backward passes that map efficiently onto modern GPU architectures. For uniformly stable second-kind discretizations, so strongly nonuniform contraction rates originate in the empirical neural tangent kernel (NTK) rather than in the discretized operator. Within each level, parameter updates can reshape the NTK, while refinement re-samples the tangent kernel on a richer discrete space and reveals directions that were not adequately resolved on coarser levels. A cross-level estimate bounds the warm-start loss in terms of the preceding training tolerance and quadrature error, motivating a tolerance schedule that balances optimization and discretization errors. Experiments on Laplace/Poisson and Helmholtz problems in two and three dimensions, together with an exterior Robin problem on a hypersurface in $\R^4$, demonstrate the method under both parametric and signed-distance surface representations at up to million-scale discretizations.

We derive an explicit bound for the L2-L2-gain of linear time-invariant systems whose output is a quadratic function of the state and the input. Such systems appear naturally in many areas, for example for port-Hamiltonian systems, optimal-control, and stochastic problems. In case the output is purely quadratic in the state, the bound is determined by the supremum over $\omega\in\mathbb R$ of the L2-norms of the bivariate transfer function restricted to the anti-diagonals $\{(s,\, i\omega-s)\mid s\in i\mathbb R\}$ of the frequency domain $i\mathbb R\times i\mathbb R $. Further, we show how the bound can be computed by solving linear matrix equations. This result provides a practical tool for assessing and reducing quadratic-output models.

Fix $0<r<1$, and let $X_1,X_2,\dots$ be independent symmetric Weibull$(r)$ random variables, that is, \[ \textsf{P}(|X_i|>t)=e^{-t^r},\qquad t\ge 0. \] We prove that there is no constant $C_r$, depending only on $r$, with the following universal property: for every finite set $T\subset \R^N$ there exists a sequence $(y_k)_{k\ge 1}\subset \R^N$ such that \[ T-T\subset conv\{y_k:k\ge 1\}, \qquad \|X_{y_k}\|_{L_{\log(k+2)}}\le C_r\,\bx(T) \quad (k\ge 1), \] where $X_t=\sum_i t_i X_i$ and $\bx(T)=\textsf{E}\sup_{t\in T}X_t$. This gives a negative answer to a question of Latała concerning the validity of convex-hull bounds for canonical Weibull processes. In fact, the failure persists even when the auxiliary vectors appearing in the convex hull are allowed to be arbitrary.

We study three-degree-of-freedom Hamiltonian systems that are invariant under rotations about the $z$-axis and under reflection across the $xy$-plane. Fixing the angular momentum, such systems reduce to Hamiltonian systems with two degrees of freedom. We focus on the range of energy values for which the corresponding Hill regions are compact. First, under suitable assumptions on the topology of these compact Hill regions, we prove the existence of periodic solutions on each prescribed energy surface of the reduced system by means of a variational minimax method. These periodic solutions are obtained as saddle points of the Maupertuis functional. The resulting solutions are either nontrivial spatial periodic solutions or trivial planar brake solutions in the reduced system. Next, by computing the Morse index, we provide a sufficient condition ensuring that the periodic solutions obtained are nontrivial. Finally, we apply our results to the isosceles three-body problem and to the spatial anisotropic Kepler problem. In both cases, we verify the sufficient condition for nontriviality and thereby establish the existence of nontrivial periodic solutions.

It has been shown that the theory of unipotent characters of finite reductive groups admits a generalisation to objects whose Weyl group is a spetsial complex reflection group, called spetses. In this paper we prove several natural properties satisfied by the unipotent characters of spetses, in particular the validity of all Harish-Chandra theories as well as the existence of Ennola $d$-alities for all integers $d$, Alvis–Curtis duality, and compatibility with Rouquier blocks of relative Hecke algebras.

Quantum contextuality is a vital non-classical resource, yet illuminating the precise mechanisms through which it enables unconditional computational advantages remains a challenge. We translate graph-theoretic formulations of contextuality into an unconditional quantum memory advantage for formal language recognition. We define a promise problem on an exclusivity graph $G$ where any classical finite automaton respecting exclusivity requires $N \ge \chi(G)$ memory states, whereas a QFA requires a memory of dimension $d = \xi(G)$. The gap between these bounds isolates a structural, information-theoretic incompatibility between classical and quantum descriptions that we term representational contextuality. For Boolean orthogonality graphs, this exacts an exponential classical memory penalty ($d=\mathcal{O}(n)$ vs $N=2^{\Omega(n)}$). Finally, we demonstrate a sharp algorithmic phase transition: allowing the classical machine a finite confusability of mutually exclusive events reduces this exponential classical memory cost to $\mathcal{O}(n)$.

Let $H$ be a connected algebraic subgroup of a connected reductive group $G$ over a finite field $\mathbb F_q$ such that $G/H$ is a $G$-spherical variety, i.e., $G/H$ has an open dense $B$-orbit for each Borel subgroup $B$ of $G$. We formulate, for the pair $(G,H)$, an almost multiplicity-one property. Then we establish a criterion for this property in terms of the $B$-stabilizers on $G/H$. In particular, we will see that this property is analogous to the strongly tempered condition in characteristic $0$.

Let A be an n x n Bernoulli random matrix whose entries are i.i.d. Bernoulli(p) random variables. In this paper, we determine the probability that the corank of A is at least k when k is of order o(sqrt(log n)): P(corank A >= k) = (1-p+o_n(1))^(kn).

We propose and analyze a nonstandard finite difference (NSFD) scheme for nonlinear parabolic equations involving a p-Laplacian-type diffusion operator in one- and two-dimensional spatial domains. Following Mickens' design principles, the proposed discretization employs a nonlinear denominator function phi(.) together with a nonlocal approximation of the nonlinear diffusion term Delta_p, yielding a structure-preserving discrete model. The scheme is designed to retain key qualitative properties of the continuous problem, including positivity, boundedness, and stability, which may be lost by standard finite difference methods (FDMs). We establish the well-posedness of the continuous model, derive the NSFD scheme, and investigate its consistency, convergence, and local truncation error. Numerical experiments confirm the theoretical results and demonstrate that, unlike the standard explicit FDM, the proposed NSFD scheme avoids spurious oscillations and nonphysical negative solutions even for relatively large time-step sizes.

Based on the growth patterns of 166 CHO monoclones observed over a 15 day period, we show that the standard population growth in a confined space equation, i.e. the sigmoid/logistic function, is alone does not capture the complex behaviour of the cell growth in a confined space. Thus, combining the sigmoid function and the exponential of the sigmoid function, we present a more accurate model for modelling cell growth in a confined space. We also present a working algorithm to obtain population growth variables (growth capacity, growth time and growth rate), model the growth patterns of the CHO monoclones, and we include subset of the dataset, along with a sample python script for the reader to replicate the results. Furthermore, we derive a model for cell confluence growth in a confined space, numerically model the confluence and present the reader with a working algorithm. With Kolmogorov-Smirnov analysis conducted on the area of the CHO monoclones, we show that the cell area of the incipient population is normally distributed, the sparse cell population is gamma distributed and the dense colony population is log-normally distributed. Thus, we further derive models for the mean, the standard deviation, the coefficient of variation and the inverse coefficient of variation for the log cell area growth in a confined space, numerically model them and present the reader with working algorithms. Finally, based on the growth patterns of another 48 CHO monoclones observed over a 16 day period, and their titer and viability measurements, we find the correlation coefficients with our calculated growth variables, and titer and viability measurements, and show that our derived growth variables can be used to predict the productivity and the health of a cell. Thus, we conclude our study by demonstrating that the productivity and the health of a cell (also the overall population) are interdependent.

Using a variational approach, we study the existence of periodic solutions with prescribed energy for the relativistic equation \begin{equation*} \dfrac{\mathrm{d}}{\mathrm{d}t}\left(\dfrac{m\dot x}{\sqrt{1-|\dot{x}|^{2}/c^{2}}}\right) = -\alpha \frac{x}{|x|^{3}} + \nabla W(x), \qquad x\in\mathbb{R}^{N}\setminus\{0\}, \end{equation*} where $W$ is a lower-order perturbation of the Kepler potential. The main difficulty stems from the fact that the Kepler singularity is critical for the associated Maupertuis functional, lying exactly at the boundary between the weak force and strong force regimes. To overcome the resulting lack of compactness, we use a penalization procedure and develop a suitable min-max scheme combined with a blow-up analysis of near-collision critical sequences. As a consequence, we establish the existence of periodic solutions on prescribed negative energy levels, obtaining non-perturbative results in every dimension $N\geq 2$.

The Potential Field Source Surface (PFSS) extrapolation is a method for estimating the large scale coronal magnetic field from photospheric magnetograms. The source surface serves as the outer boundary of its solution domain, and is typically a spherical surface. An appropriate source surface radius ($R_{ss}$) enables more accurate identification of the coronal magnetic field topology and estimation of the open flux, thereby potentially enhancing the accuracy of space weather modeling. We prove the well-posedness of the PFSS forward problem and establish the existence and uniqueness of the optimal source surface by combining compactness of the admissible set with continuity of the objective functional. The objective functional is the mean squared error (MSE) between PFSS extrapolation and Parker Solar Probe (PSP) radial magnetic field measurements after Parker spiral backmapping and radial scaling for Encounters 1-19. The optimization algorithm is validated with an analytical solution, and Advanced Composition Explorer (ACE) in situ measurements are used as an independent cross-validation dataset. Additional evaluation metrics and Pareto analysis are used to identify the dominant metrics between open flux and polarity prediction accuracy. Our results show that the optimal $R_{ss}$ derived from the algorithm generally increase from solar minimum into the ascending phase of solar cycle 25. The optimized solution improves open flux agreement while preserving or improving polarity prediction accuracy relative to $2.5R_{s}$. The Pareto frontiers show a transition for dominant metrics from open flux during solar minimum to polarity prediction accuracy during the ascending phase.

We study existence, regularity, and uniqueness for the nonlinear kinetic Fokker–Planck equation $$ \partial_t f=\Delta_v\Psi(f)-v\cdot\nabla_x f, \qquad f|_{t=0}=f_0, $$ on $\mathbb R^{2d}$. In the model case $\Psi(r)=r^s$, this equation couples nonlinear fast-diffusion/porous-medium type diffusion with kinetic transport. A distinctive feature is that the diffusion acts only in the velocity variable $v$, so that compactness in the spatial variable $x$ cannot be obtained from standard elliptic estimates and must instead be recovered through the hypoelliptic structure. Under general structural assumptions on $\Psi$, including the fast-diffusion powers $\Psi(r)=r^s$ with $s\in(0,1)$, we construct nonnegative weak solutions and prove quantitative anisotropic Besov regularity estimates. Under an additional mass-critical growth condition on the fast-diffusion side, the constructed weak solution preserves mass, admits a renormalized kinetic formulation, and is unique in the $L^1$-class of mass-preserving renormalized kinetic solutions. In the power-law case $\Psi(r)=r^s$, this condition is precisely $s\ge 1-1/d$ when $d\ge2$, while in dimension $d=1$ the whole fast-diffusion range $s\in(0,1)$ is covered. The main analytic ingredient is a parameter-dependent smoothing estimate for the kinetic semigroup generated by $$ \Psi'(\zeta)\Delta_v - v\cdot\nabla_x , $$ which quantitatively tracks the dependence on the kinetic level $\zeta$. Combined with the kinetic formulation, this estimate yields compactness in both spatial and velocity variables for the nonlinear hypoelliptic problem. As an application, we also obtain martingale-problem solutions to the associated distributional-density dependent stochastic differential equation.

Let $\mathcal Q=\{Q_a:a\geq1\}$ be a nested family of finite posets such that $Q_a\subseteq Q_{a+1}$ and $|Q_a|<|Q_{a+1}|$. For a poset $Q$, let $\mathcal C_t(Q)$ denote the set of all strict $t$-chains in $Q$. Given an $r$-coloring of $\mathcal C_t(Q_a)$ and posets $P_1,\ldots,P_r$, a weak copy of $P_i$ is called monochromatic of color $i$ if all $t$-chains in the copy have color $i$; the strong version is defined in the same way for induced copies. The corresponding weak and strong multiplicity parameters are the minimum possible total number of such monochromatic copies in the host poset.For the Boolean lattice $B_n$, define $E_n={(S,T,U)\in B_n^3:S\subsetneq T\subsetneq U,\ |S|+|T|=|U|}.$ For a two-coloring $\chi:B_n\to{0,1}$, a triple $(S,T,U)\in E_n$ is monochromatic if $\chi(S)=\chi(T)=\chi(U)$. Let $R^{\mathrm{arith}}_2$ be the least integer $n$ such that every two-coloring of $B_n$ contains a monochromatic triple in $E_n$, and let $M^{\mathrm{arith}}_2(B_n)$ be the minimum number of monochromatic triples in $E_n$ over all two-colorings of $B_n$. We prove that $R^{\mathrm{arith}}_2=9.$ Moreover, $|E_n|=\binom{2n}{n}-[x^n](1+x+x^2)^n-2^n+1=\frac{4^n}{\sqrt{\pi n}}\bigl(1+o(1)\bigr),$ and $2^{\delta n+o(n)}\le M^{\mathrm{arith}}_2(B_n)\le 2^{\gamma n+o(n)}, $ where $\delta\approx 1.356779$ and $\gamma\approx 1.567837$ are explicit entropy constants. For general nested host families, we prove a double-counting lower bound for strong poset multiplicity. For an arbitrary finite host poset $R$, we also introduce a Fourier-Möbius method and give an exact Fourier expansion for strong multiplicity, a Parseval-type error bound, and a spectral lower bound.

This paper proposes an inner–outer (IO) iterative algorithm with optimal parameters for solving stochastic Lyapunov matrix equation associated with discrete-time stochastic linear system. First, under the assumption that the underlying stochastic linear system is asymptotically mean-square stable, the monotonicity and boundedness of the iterative sequence generated by the proposed algorithm are analyzed. On this basis, a sufficient convergence result is established for the zero initial condition. Second, by deriving the spectral radius of the corresponding iteration matrix, several necessary and sufficient convergence conditions are obtained for arbitrary initial conditions. In addition, the optimal parameter-selection strategies are developed to improve the convergence performance of the algorithm. Finally, numerical examples are presented to verify the theoretical results and demonstrate the advantages of the proposed algorithm over several existing iterative methods.