arXiv Math @arxiv_math@qoto.org

Given an $r$-uniform hypergraph $H$ and a positive integer $n$, the weak saturation number $\mathrm{wsat}(n,H)$ is the minimum number of edges in an $r$-uniform hypergraph $F$ on $n$ vertices such that the missing edges in $F$ can be added, one at a time, so that each added edge creates a copy of $H$. Shapira and Tyomkyn (Proceedings of the American Mathematical Society, 2023) proved Tuza's conjecture on asymptotic behaviour of $\mathrm{wsat}(n, H)$. In this paper we provide a significantly shorter proof of the conjecture.

In this paper we investigate the following related problems: (A) the separation of $p$-adic roots of integer polynomials of a fixed degree and bounded height; and (B) counting integer polynomials of a fixed degree and bounded height with discriminant divisible by a (large) power of a fixed prime. One of the consequences of our findings is the existence, for all large $Q>1$, of $Q^{2/n}$ integer irreducible polynomials $P$ of degree $n$ and height $\asymp Q$ with an almost prime power discriminant of maximal size, that is $|D(P)|\asymp Q^{2n-2}$ and $D(P)=p^kC_P$ with $C_P\in\mathbb{Z}$ satisfying $|C_P|\ll1$. The method we use generalises the techniques used in the study of the real case [Beresnevich, Bernik and Götze, 2010 and 2016] and relies on a quantitative non-divergence estimate developed by Kleinbock and Tomanov.

Heat exchangers are critical components in a wide range of engineering applications, from energy systems to chemical processing, where efficient thermal management is essential. The design objectives for heat exchangers include maximizing the heat exchange rate while minimizing the pressure drop, requiring both a large interface area and a smooth internal structure. State-of-the-art designs, such as triply periodic minimal surfaces (TPMS), have proven effective in optimizing heat exchange efficiency. However, TPMS designs are constrained by predefined mathematical equations, limiting their adaptability to freeform boundary shapes. Additionally, TPMS structures do not inherently control flow directions, which can lead to flow stagnation and undesirable pressure drops. This paper presents DualMS, a novel computational framework for optimizing dual-channel minimal surfaces specifically for heat exchanger designs in freeform shapes. To the best of our knowledge, this is the first attempt to directly optimize minimal surfaces for two-fluid heat exchangers, rather than relying on TPMS. Our approach formulates the heat exchange maximization problem as a constrained connected maximum cut problem on a graph, with flow constraints guiding the optimization process. To address undesirable pressure drops, we model the minimal surface as a classification boundary separating the two fluids, incorporating an additional regularization term for area minimization. We employ a neural network that maps spatial points to binary flow types, enabling it to classify flow skeletons and automatically determine the surface boundary. DualMS demonstrates greater flexibility in surface topology compared to TPMS and achieves superior thermal performance, with lower pressure drops while maintaining a similar heat exchange rate under the same material cost.

This work explores a novel perspective on solving nonconvex and nonsmooth optimization problems by leveraging sampling based methods. Instead of treating the objective function purely through traditional (often deterministic) optimization approaches, we view it as inducing a target distribution.We then draw samples from this distribution using Markov Chain Monte Carlo (MCMC) techniques, particularly Langevin Dynamics (LD), to locate regions of low function values. By analyzing the convergence properties of LD in both KL divergence and total variation distance, we establish explicit bounds on how many iterations are required for the induced distribution to approximate the target. We also provide probabilistic guarantees that an appropriately drawn sample will lie within a desired neighborhood of the global minimizer, even when the objective is nonconvex or nonsmooth.

Unconstrained optimization problems become more common in scientific computing and engineering applications with the rapid development of artificial intelligence, and numerical methods for solving them more quickly and efficiently have been getting more attention and research. Moreover, an efficient method to minimize all kinds of objective functions is urgently needed, especially the nonsmooth objective function. Therefore, in the current paper, we focus on proposing a novel numerical method tailored for unconstrained optimization problems whether the objective function is smooth or not. To be specific, based on the variational procedure to refine the gradient and Hessian matrix approximations, an efficient quadratic model with $2n$ constrained conditions is established. Moreover, to improve the computational efficiency, a simplified model with 2 constrained conditions is also proposed, where the gradient and Hessian matrix can be explicitly updated, and the corresponding boundedness of the remaining $2n-2$ constrained conditions is derived. On the other hand, the novel numerical method is summarized, and approximation results on derivative information are also analyzed and shown. Numerical experiments involving smooth, derivative blasting, and non-smooth problems are tested, demonstrating its feasibility and efficiency. Compared with existing methods, our proposed method can efficiently solve smooth and non-smooth unconstrained optimization problems for the first time, and it is very easy to program the code, indicating that our proposed method not also has great application prospects, but is also very meaningful to explore practical complex engineering and scientific problems.

In multi-objective optimization, minimizing the worst objective can be preferable to minimizing the average objective, as this ensures improved fairness across objectives. Due to the non-smooth nature of the resultant min-max optimization problem, classical subgradient-based approaches typically exhibit slow convergence. Motivated by primal-dual consensus techniques in multi-agent optimization and learning, we formulate a smooth variant of the min-max problem based on the augmented Lagrangian. The resultant Exact Pareto Optimization via Augmented Lagrangian (EPO-AL) algorithm scales better with the number of objectives than subgradient-based strategies, while exhibiting lower per-iteration complexity than recent smoothing-based counterparts. We establish that every fixed-point of the proposed algorithm is both Pareto and min-max optimal under mild assumptions and demonstrate its effectiveness in numerical simulations.

Multi-objective learning under user-specified preference is common in real-world problems such as multi-lingual speech recognition under fairness. In this work, we frame such a problem as a semivectorial bilevel optimization problem, whose goal is to optimize a pre-defined preference function, subject to the constraint that the model parameters are weakly Pareto optimal. To solve this problem, we convert the multi-objective constraints to a single-objective constraint through a merit function with an easy-to-evaluate gradient, and then, we use a penalty-based reformulation of the bilevel optimization problem. We theoretically establish the properties of the merit function, and the relations of solutions for the penalty reformulation and the constrained formulation. Then we propose algorithms to solve the reformulated single-level problem, and establish its convergence guarantees. We test the method on various synthetic and real-world problems. The results demonstrate the effectiveness of the proposed method in finding preference-guided optimal solutions to the multi-objective problem.

This research article explores the optimization of aluminium extrusion processes through advanced line balancing techniques, focusing on maximizing marginal profit by increasing melting and casting outputs. By employing mixed integer linear programming (MILP), we identify strategies to minimize idle costs and enhance production efficiency. The study demonstrates that increasing the daily cycle rate from 2 to 4.36 cycles results in a significant rise in daily marginal profit, calculated at USD67,786, after accounting for additional labour costs. This optimization is achieved by expanding the workforce from 8 to 12 operators across two shifts, leading to a 50% increase in labour expenses. The findings reveal a remarkable 117.6% growth in marginal daily profit, underscoring the potential of automation and intelligent manufacturing in transforming the aluminium extrusion industry. Insights from cross-industry research, including Lean Methodology in the Modern Garment Industry, further illustrate the broader applicability of these advancements. This study highlights the critical role of automation in driving productivity and profitability in manufacturing sectors, paving the way for future innovations in aluminium extrusion and beyond.

We explore convex shapes $S$ in the Euclidean plane which have the following property: there is a circle $C$ such that the angle between the two tangents from any point of $C$ to $S$ is constant equal to $α$. A dynamical formulation allows to analyze the existence of such shapes. Interestingly, the existence of non-circular shapes depends in a non-trivial way on the angle $α$.

This work presents a massive SIMO scheme for wireless communications with one-shot noncoherent detection. It is based on permutational index modulation over OFDM. Its core principle is to convey information on the ordering in which a fixed collection of values is mapped onto a set of OFDM subcarriers. A spherical code is obtained which provides improved robustness against channel impairments. A simple detector based on the sorting of quadratic metrics of data is proposed. By exploiting statistical channel state information and hardening, it reaches a near-ML error performance with a low-complexity implementation.

We exploit the multiplicative structure of Pólya Tree priors for density and differential entropy estimation in $p$-dimensions. We establish: (i) a representation theorem of entropy functionals and (ii) conditions on the parameters of Pólya Trees to obtain Kullback-Leibler and Total Variation consistency for vectors with compact support. Those results motivate a novel differential entropy estimator that is consistent in probability for compact supported vectors under mild conditions. In order to enable applications of both results, we also provide a theoretical motivation for the truncation of Univariate Pólya Trees at level $3 \log_2 n $.

We prove that if $(A,\mathfrak{m})$ is a local ring of mixed characteristic $(0,p)$ and $A/pA$ is an analytically irreducible $F$-pure Gorenstein domain, then $A$ is perfectoid pure. Along the way, we give a list of sufficient conditions for a complete Gorenstein local domain of mixed characteristic to be the completion of a splinter.

In this paper, we present a general framework for the derivation of interesting finite combinatorial sums starting with certain classes of polynomial identities. The sums that can be derived involve products of binomial coefficients and also harmonic numbers and squared harmonic numbers. We apply the framework to discuss combinatorial sums associated with some prominent polynomial identities from the recent past.

Two kinds of novel generalizations of Nesbitt's inequality are explored in various cases regarding dimensions and parameters in this article. Some other cases are also discussed elaborately by using the semiconcave-semiconvex theorem. The general inequalities are then employed to deduce some alternate inequalities and mathematical competition questions. At last, a relation about Hurwitz-Lerch zeta functions is obtained.

Can you find an xy-equation that, when graphed, writes itself on the plane? This idea became internet-famous when a Wikipedia article on Tupper's self-referential formula went viral in 2012. Under scrutiny, the question has two flaws: it is meaningless (it depends on typography) and it is trivial (for reasons we will explain). We fix these flaws by formalizing the problem, and we give a very general solution using techniques from computability theory.

In this paper, we give a description of the self-injective dimension of string algebras and obtain a necessary and sufficient condition for a string algebra to be Gorenstein.

The OEIS sequence A051221 consists of nonnegative integers of the form 10^x - y^2. The known values are those less than or equal to 2000 with x <= 7, and it is conjectured that no new values in this range appear for x >= 8. In this paper, we give an elementary proof that this is indeed the case by using Pell-type equations.

We investigate forms of filter extension properties in the two-cardinal setting involving filters on $P_κ(λ)$. We generalize the filter games introduced by Holy and Schlicht in \cite{HolySchlicht:HierarchyRamseyLikeCardinals} to filters on $P_κ(λ)$ and show that the existence of a winning strategy for Player II in a game of a certain length can be used to characterize several large cardinal notions such as: $λ$-super/strongly compact cardinals, $λ$-completely ineffable cardinals, nearly $λ$-super/strongly compact cardinals, and various notions of generic super and strong compactness. We generalize a result of Nielson from \cite{NielsenWelch:games_and_Ramsey-like_cardinals} connecting the existence of a winning strategy for Player II in a game of finite length and two-cardinal indescribability. We generalize the result of \cite{ForMagZem} to construct a fine $κ$-complete precipitous ideal on $P_κ(λ)$ from a winning strategy for Player II in a game of length $ω$. Finally, we improve Theorems 1.2 and 1.4 from \cite{ForMagZem} and partially answer questions Q.1 and Q.2 from \cite{ForMagZem}.

We introduce the notion of the weak Brehmer's condition and prove that the Cauchy transform for a representation of a right-angled Artin monoid is bounded under such conditions. As a result, we obtain the Poisson transform and $*$-regular dilation for a family of operators that satisfies the weak Brehmer's condition and the property (P). This generalizes Popescu's notion of Cauchy and Poisson transforms for commuting families of row contractions.

We look at the question of which distance-regular graphs are core-complete, meaning they are isomorphic to their own core or have a complete core. We build on Roberson's homomorphism matrix approach by which method he proved the Cameron-Kazanidis conjecture that strongly regular graphs are core-complete. We develop the theory of the homomorphism matrix for distance-regular graphs of diameter $d$. We derive necessary conditions on the cosines of a distance-regular graph for it to admit an endomorphism into a subgraph of smaller diameter $e<d$. As a consequence of these conditions, we show that if $X$ is a primitive distance-regular graph where the subgraph induced by the set of vertices furthest away from a vertex $v$ is connected, any retraction of $X$ onto a diameter-$d$ subgraph must be an automorphism, which recovers Roberson's result for strongly regular graphs as a special case for diameter $2$. We illustrate the application of our necessary conditions through computational results. We find that no antipodal, non-bipartite distance-regular graphs of diameter 3, with degree at most $50$ admits an endomorphism to a diameter 2 subgraph. We also give many examples of intersection arrays of primitive distance-regular graphs of diameter $3$ which are core-complete. Our methods include standard tools from the theory of association schemes, particularly the spectral idempotents. Keywords: algebraic graph theory, distance-regular graphs, association schemes, graph homomorphisms