arXiv Math @arxiv_math@qoto.org

In this paper are examined general classes of linear and non-linear analytical systems of partial differential equations. Indeed the integrability conditions are found and if they are satisfied, the solutions are given as functional series in a neighborhood of a given point (x=0).

In this paper, we prove Raabe-type integral formulas for gamma function via left and right sided Riemann-Liouville fractional integrals. As corollaries, we give the left and right sided repeated integration formulas for the log-gamma and related functions. The relationship between the generalized Glaisher constants and aforementioned integrals are investigated.

If you only take one thing away from this document I would like it tobe this: Creating accessible documents requires authors have a working understanding of accessibility, and write with accessibility in mind.

A sequence $\Big(u_n\Big)_{n=0}^{\infty}$ is said to be convex if it satisfies the following inequality $$ 2u_n\leq u_{n-1}+u_{n+1}\qquad \mbox{for all}\qquad n\in\mathbb{N}. $$ We present several characterizations of convex sequences and demonstrate that such sequences can be locally interpolated by quadratic polynomials. Furthermore, the converse assertion of this statement is also established. On the other hand, a sequence $\Big(u_n\Big)_{n=1}^{\infty}$ is called approximately subadditive if for a fixed $ε>0$ and any partition $n_1,\cdots,n_k$ of $n\in\mathbb{N}$; the following discrete functional inequality holds true $$ u_n\leq u_{n_1}+\cdots+ u_{n_k}+\varepsilon. $$ We show Ulam's type stability result for such sequences. We prove that an approximately subadditive sequence can be expressed as the algebraic summation of an ordinary subadditive and a non-negative sequence bounded above by $\varepsilon.$ A proposition portraying the linkage between the convex and subadditive sequences under minimal assumption is also included.

The main goal of this research is to model and investigate generalizations of functions from [31]. Arguments of modeled functions are presented by the representation $π_{\mathfrak p}$ from [22].

In this chapter, I discuss teaching mathematical tools specifically tailored for economics students. A typical one-semester course in this area seeks to blend a range of topics: from foundational elements of subjects such as linear algebra and multivariate calculus to intermediate areas like real and convex analysis and further into advanced topics such as dynamic optimization in both continuous and discrete time. This breadth of coverage corresponds to material usually spread across multiple years in traditional mathematics programs. Given the comprehensive nature of these courses, careful selection of topics is essential, balancing numerous trade-offs. I discuss potential course sequences and instructional design choices. I then focus on conceptualizing and explaining mathematical modeling in economics. I reflect on three years of teaching an advanced undergraduate course in mathematical methods online. The latter part of the chapter offers examples and visualizations I have found particularly beneficial for imparting intuition to economics students. They cover a range of topics at different degrees of difficulty and are meant as a resource for instructors in Mathematics for Economists. Among these, I use the Ramsey model as a recurring example, especially relevant when designing a mathematical tools course with an orientation towards preparing students for macroeconomic analysis.

The present work is concerned with Gaussian integrals on simply-connected non-positively-curved Riemannian symmetric spaces. It is motivated by the aim of explicitly finding the high-rank limit of these integrals for each of the eleven families of classical Riemannian symmetric spaces. To begin, it deals with the easier complex case (where the isometry group admits a complex Lie group structure). To go beyond this case, it introduces a variational characterisation of the high-rank limit, as the minimum of a certain energy functional over the space of probability distributions on the real line. Using this new variational formulation, it is possible to recover the high-rank limit in closed form, from the expression originally found in the complex case. This two-step approach is illustrated through the examples of two kinds of symmetric spaces : symmetric cones and classical symmetric domains.

We focus on the new type perturbed metric spaces and introduce a contraction mapping namely new type perturbed Kannan mappings. For these mappings, we show that Banach's fixed point theorem holds. Moreover, this new generalization of Banach's contraction principle does not depend on the continuity of the operator.

We analyze a sequential quadratic programming algorithm for solving a class of abstract optimization problems. Assuming that the initial point is in an $L^2$ neighborhood of a local solution that satisfies no-gap second-order sufficient optimality conditions and a strict complementarity condition, we obtain stability and quadratic convergence in $L^q$ for all $q\in[p,\infty]$ where $p\geq 2$ depends on the problem. Many of the usual optimal control problems of partial differential equations fit into this abstract formulation. Some examples are given in the paper. Finally, a computational comparison with other versions of the SQP method is presented.

In this paper, we apply the concept of asymptotic dimension to calculating Gromov-Hausdorff distances between some unbounded metric spaces. For example, we show that the Gromov--Hausdorff between $\mathbb{R}^2$ with the Euclidean metric and $\mathbb{Z}^2$ equals the Hausdorff distance between them: $d_{GH}(\mathbb{R}^2, \mathbb{Z}^2) = d_H(\mathbb{R}^2, \mathbb{Z}^2)$.

We present a method for constructing all bounded rational motions that frame a space curve $\mathbf{r}(t)$. This means that the motion guides an orthogonal frame along the curve such that one frame axis is in direction of the curve tangent. Existence of (bounded) framing motions is equivalent to $\mathbf{r}(t)$ being a (bounded) rational Pythagorean Hodograph curve. In contrast to previous constructions that rely on polynomial curves with smooth self-intersection, our motions and curves are infinitely differentiable. To this end, we develop the theory of Pythagorean hodograph curves parameterized over the projective line. We also provide a simple geometric necessary and sufficient condition on the spherical part of the motion, given by the homogeneous quaternionic preimage of the Pythagorean hodograph curve, that ensures the existence of a corresponding bounded, rational, and even regular framing motion. The translation part comes from the speed distribution, which must be a special positive rational function. This can in practice be ensured by semidefinite optimization methods. We illustrate our findings with a number of examples.

Departure time management is an efficient way in addressing the peak-hour crowding in public transport by reducing the temporal imbalance between service supply and travel demand. From the demand management perspective, the problem is to determine an equilibrium distribution of departure times for which no user can reduce their generalized cost by changing their departure times unilaterally. This study introduces the departure time choice user equilibrium problem in public transport (DTUE-PT) for multi-line, schedule-based networks with hard train capacity constraints. We model the DTUE-PT problem as a Non-linear Mathematical Program problem (NMP) (minimizing the system gap) with a simulation model describing the complex system dynamics and passenger interactions. We develop an efficient, adaptive gap-based descent direction (AdaGDD) solution algorithm to solve the NMP problem. We validate the methodology on a multi-line public transport network with transfers by comparing with classical public transport assignment benchmark models, including Method of Successive Average (MSA) and day-to-day learning methods. The results show that the model can achieve a system gap ratio (the solution gap relative to the ideal least cost of an origin-destination option) of 0.1926, which significantly improves the solution performance from day-to-day learning (85%) and MSA (76%) algorithms. The sensitivity analysis highlights the solution stability of AdaGDD method over initial solution settings. The potential use of DTUE-PT model is demonstrated for evaluating the network design of Hong Kong mass transit railway network and can be easily extended to incorporate the route choice.

This paper investigates the generative mechanism of the p-order cloud model, which is a mathematical framework for representing uncertainty with applications in image processing, evaluation, and decision-making systems. By employing a reparameterization technique, we reformulate the cloud model as a stochastic recurrence equation (SRE) with a nonlinear transformation involving an absolute value. Under standard assumptions of stationarity, ergodicity, and an appropriate integrability condition, we establish the existence and uniqueness of a stationary solution. In particular, we demonstrate that the logarithmic moment of the model's coefficient, modeled as a standard normal random variable, is negative, thereby ensuring almost sure convergence. These results provide new insights into the stochastic stability of cloud models and offer a rigorous foundation for further theoretical and practical developments in uncertainty quantification.

In the context of PDE-constrained optimization theory, source identification problems traditionally entail particles emerging from an unknown source distribution inside a domain, moving according to a prescribed stochastic process, e.g.~Brownian motion, and then exiting through the boundary of a compact domain. Given information about the flux of particles through the boundary of the domain, the challenge is to infer as much as possible about the source. In the PDE setting, it is usually assumed that the flux can be observed without error and at all points on the boundary. Here we consider a different, more statistical presentation of the model, in which the data has the form of discrete counts of particles arriving at a set of disjoint detectors whose union is a strict subset of the boundary. In keeping with the primacy of the stochastic processes in the generation of the model, we present a gradient descent algorithm in which exit rates and parameter sensitivities are computed by simulations of particle paths. We present examples for both Itô diffusion and piecewise-deterministic Markov processes, noting that the form of the sensitivities depends only on the parameterization of the source distribution and is universal among a large class of Markov processes.

This study explores the application of Pontryagin's Maximum Principle to derive optimal strategies for controlling the spread of COVID-19, leveraging a novel compartmental model to capture the disease dynamics. We prioritize three key criteria: cost, effectiveness, and feasibility, each examined independently to evaluate their unique contributions to pandemic management. By addressing these criteria, this study aims to design intervention strategies that are scientifically robust, practical, and economically sustainable. Furthermore, the focus on cost, effectiveness and feasibility seeks to provide policymakers with actionable insights for implementing interventions that maximize public health benefits while remaining feasible under real-world conditions.

We consider the inverse dynamic problem for the wave equation with a potential on an interval $(0,2π)$ with periodic boundary conditions. We use a boundary triplet to set up the initial-boundary value problem. As an inverse data we use a response operator (dynamic Dirichlet-to-Neumann map). Using the auxiliary problem on the whole line, we derive equations of the inverse problem. We also establish the relationships between dynamic and spectral inverse data.

This work concerns notions of multi-algebra independence introduced by Liu and how they can be studied in the context of bi-free probability. In particular, we show how the free-free-Boolean independence for triples of algebras can be embedded intro and therefore studied from a lens of bi-free probability. It is also shown how its cumulants can be constructed from the bi-free cumulants.

The behavior of wave signals in the far zone is not only of theoretical interest but also of paramount practical importance in communications and other fields of applications of optical, electromagnetic or acoustic waves. Long time ago T. T. Wu introduced models of 'electromagnetic missiles' whose decay could be made arbitrarily slower than the usual inverse distance by an appropriate choice of the high frequency portion of the source spectrum. Very recent work by Plachenov and Kiselev introduced a finite-energy scalar wave solution, different from Wu's, decaying slower than inversely proportional with the distance. A physical explanation for the unusual asymptotic behavior of the latter will be given in this article. Furthermore, two additional examples of scalar wave pulses characterized by abnormal slow decay in the far zone will be given and their asymptotic behavior will be discussed. A proof of feasibility of acoustic and electromagnetic fields with the abnormal asymptotics will be described.

We study from a statistical mechanics viewpoint some of the simplest mathematical objects, finite pure sets. Starting from the empty set, new generations are produced step by step, sets of the next generation being those whose elements are the sets of the current generation. We compute in particular correlations and limiting laws for chains of various lengths for the membership relation when the number of generations becomes large.