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Dragos-Patru Covei

Publications and source records attributed to Dragos-Patru Covei.

At least 19 recordsLinked to original sources

Entire Large Solutions for Competitive Semilinear Elliptic Systems with General Nonlinearities Satisfying Keller--Osserman Conditions

We study positive radial solutions of the weighted cooperative system $Δu=p(|x|)f(u,v)$, $Δv=q(|x|)g(u,v)$ in $\mathbb{R}^{n}$, $n\geq3$, when the Newtonian potentials of the nonnegative weights are finite and the nonlinear source possesses a Keller--Osserman lower envelope. A threshold set of central data is introduced and its order and compactness properties are used to construct an entire solution on its interior boundary. A decreasing nonlinear transform applied to $W=u+v$ yields an explicit lower estimate in terms of the tail Newtonian potential and proves that $W(r)\to\infty$. Consequently at least one component is large. We also identify the additional hypothesis genuinely needed for componentwise blow-up: diagonal Keller--Osserman integrability alone is insufficient, whereas a two-sided balance of the weighted reaction channels forces $u(r),v(r)\to\infty$. Two independent proofs of this transfer principle are given, one based on radial fluxes and one on Newtonian potentials. Finally, the PDE system is derived from an isothermal two-species diffusion--reaction balance in a heterogeneous porous catalyst, including nondimensional parameters and the interpretation of the whole-space idealization.

math.AP

Spectral Riccati--Gamma Concavity, Symmetric Zero Cancellation, and Conditional Criteria for the Riemann Hypothesis

We examine a Riccati--Gamma approach to the logarithmic derivative of the completed Riemann zeta function. The first part proves, in full local detail, that a naive two-sided vertical concavity criterion for $Ξ'/Ξ$ cannot be a proof of the Riemann Hypothesis, because every zero produces opposite vertical curvatures on the two horizontal sides of the pole of the logarithmic derivative. The second part replaces this obstruction by a rigorously formulated finite spectral averaging framework. We prove cancellation at the critical line, positivity of the off-critical paired contribution on the left of the critical line under a concrete low-frequency kernel condition, a conditional zero-density consequence, and a precise conditional theorem showing which additional localisation hypotheses would imply the Riemann Hypothesis. The results are therefore not presented as an unconditional proof of RH. They give a partial resolution of the Riccati--Gamma question: one natural route is ruled out unconditionally, a second symmetric mechanism is proved at the finite spectral level, and the remaining step is isolated as explicit analytic hypotheses. Reproducible Python routines and numerical figures accompany the analytic discussion.

math.GM

Asymptotic Plateaus for Generalized Abel Equations with Financial Applications

We develop a unified analytical and computational framework for the generalized Abel ordinary differential equation $y^{\prime }(x)=a_n(x)\bigl(% y^n+λ_{n-1}(x)y^{n-1}+\dots+λ_0(x)\bigr)$ of arbitrary degree $% n\ge1$ on the unbounded interval $[x_0,\infty)$. Under mild structural hypotheses on the coefficients and on the existence of a stable moving equilibrium branch $E(x)$, we prove a new \emph{Asymptotic Plateau Theorem} establishing that the solution issued from $y(x_0)=0$ is globally defined, strictly monotone, trapped between zero and $E(x)$, and converges to a finite positive limit $L=\lim_{x\to\infty}E(x)$. We further obtain an explicit, computable rate of convergence and a degree-reduction principle that generalizes the classical Liouville substitution. The theory is complemented by a high-order Radau IIA implementation whose output reproduces the predicted plateaus to nine significant digits. A detailed application to a generalized Merton structural credit-risk model derives an Abel-type equation for the long-maturity state profile of the credit spread and illustrates the economic relevance of the framework.

math.NA

Riccati--Gamma Dynamics for Concavity and Asymptotics of Generalized Dirichlet Eta Functions

We develop a unified analytical and dynamical framework for the qualitative study of the one-parameter family of generalized Dirichlet eta functions $η_{a}(t)=\sum_{m\ge0}(-1)^{m}(am+1)^{-t}$, $a>0$, $t>0$, which includes the classical Dirichlet eta and beta functions. Using a Mellin--Laplace representation of $η_{a}$ as $\mathbb{E}[f_{a}(X_{t})]$, where $f_{a}$ is a scaled logistic function and $(X_{t})$ a standard Gamma process, we show that the logarithmic derivative $φ_{a}(t)=η_{a}'(t)/η_{a}(t)$ satisfies a non-homogeneous Riccati equation with strictly negative forcing. This single inequality yields strict concavity and strict log-concavity of $η_{a}$, positivity and monotonicity of $φ_{a}$, and the precise asymptotic law $φ_{a}(t)=\log(a+1)(a+1)^{-t}+O((a+2)^{-t})$. We further prove that $φ_{a}(t)/φ_{a,e}(t)\to 2/\log(a+1)$ as $t\to\infty$, where $φ_{a,e}(t)=-η_{a}''(t)/(2η_{a}(t))$, obtaining in particular the trapping inequality $0<φ_{a,e}(t)<φ_{a}(t)$ for all sufficiently large $t$ when $a<e^{2}-1$. We also present a self-contained geometric-rate algorithm (rate $1/3$) for computing $η_{a}^{(k)}(t)$ together with a sharp error bound. High-precision numerical experiments confirm all results. As an application, we show that the Riccati--Gamma dynamics of $η_{a}$ and $φ_{a}$ provide a principled mechanism for musical synthesis, generating a complete melody whose pitch and rhythm are governed by these functions.

math.GM

Optimal Production Planning Under Macroeconomic Regime Switches

We develop a comprehensive mathematical and computational framework for optimal production planning in economies governed by stochastic regime switches driven by a continuous-time Markov chain. The value functions of the underlying stochastic control problem satisfy a weakly coupled system of quasilinear elliptic Hamilton--Jacobi--Bellman (HJB) equations. We establish a global multi-regime comparison principle and prove the existence and uniqueness of classical solutions under optimal growth conditions, generalizing the scalar framework to the multi-regime setting. Furthermore, we derive exact, radially symmetric quadratic solutions for both the scalar and the fully coupled HJB systems, providing explicit, dimension-free representations of the optimal value functions and production policies. These theoretical results are applied to a two-regime economy to analyze time consistency and the sensitivity of optimal responses to macroeconomic parameters. The article is supplemented by numerical implementations that validate the analytical findings and demonstrate the regime-switching dynamics.

math.AP

The Triality of Radial Nonlinear Dynamics: Analysis of Riccati, Schrödinger, and Hamilton--Jacobi--Bellman Equations

This study develops a unified mathematical framework for the analysis of radial differential equations, revealing a fundamental connection between three distinct classes of problems: the nonlinear Riccati equation, the linear Schrödinger equation, and the Hamilton--Jacobi--Bellman equation for stochastic control. We establish the existence and uniqueness of regular solutions on both bounded and unbounded domains, deriving sharp growth rates and exact asymptotic plateaus through a general barrier theory. A detailed sensitivity analysis of the noise intensity parameter identifies the transition between deterministic and diffusion-dominated regimes via singular perturbation methods. These theoretical results are reinforced by numerical simulations that validate the predicted feedback laws, confirm the convexity--concavity structure of the triality, and illustrate the stability of the system. The resulting framework clarifies the duality between global wave functions and local dynamical drifts, providing a rigorous foundation for the study of multidimensional stochastic processes under central potentials.

math.AP

Existence, Sharp Boundary Asymptotics, and Stochastic Optimal Control for Semilinear Elliptic Equations with Gradient-Dependent Terms and Singular Weights: Theory, Economic Applications, and Numerical Simulations

We develop a unified framework for semilinear elliptic equations with gradient-dependent nonlinearities and singular weights in strictly convex domains. Considering large solutions of \[ -Δu + b(x)\,h(|\nabla u|) + a(x)\,u = f(x) \quad \text{in }Ω\subset\mathbb{R}^N, \] where $h$ is strictly convex with $h(s)\sim s^{q}$ for $q\in(1,2]$ and $a(x),b(x)$ display prescribed singular behavior near $\partialΩ$, we establish existence and uniqueness via Perron's method and derive sharp boundary blow-up asymptotics. In particular, we obtain optimal liminf--limsup estimates for the rate $γ=(β-q+2)/(q-1)$, extending recent analytic techniques for gradient-growth problems. We further prove strict convexity of solutions through the microscopic convexity principle, highlighting geometric effects arising from the interaction between the nonlinear gradient term and the singular weights. A verification theorem is provided, showing that the solution coincides with the value function of an infinite-horizon stochastic optimal control problem with state constraints. Applications to Operational Research and Management Science are discussed, including case studies in inventory control, portfolio risk management, and supply chain modeling, as well as connections with boundary layer theory and diffusion processes. Numerical experiments in one and two dimensions confirm the predicted boundary behavior and geometric structure. Python implementations are included in the Appendix.

math.AP

A Bernoulli-Type Localization Principle for Nonlinear Diffusion Equations: Structural Description of Solutions and First Application to Image Processing

This paper develops a new method for analyzing nonlinear diffusion equations of porous--medium type with time-dependent growth rates, based on the \emph{localization of solutions} through an associated Bernoulli-type ordinary differential equation. This \emph{Bernoulli localization principle} separates the temporal and spatial components of the dynamics: the time evolution is governed explicitly by a Bernoulli ODE with a closed-form solution, while the spatial profile is determined by a stationary sublinear elliptic equation of Brezis--Oswald type. This decomposition replaces traditional qualitative analysis of the full parabolic PDE with a transparent combination of a scalar ODE and an elliptic problem, yielding a complete structural description of all solutions. We establish three main results: (i) existence and uniqueness of separable classical solutions obtained via the Bernoulli transformation; (ii) a comparison principle ensuring that orderings of initial data and growth rates persist in time; and (iii) existence and uniqueness of general, non-separable solutions through monotone iteration with explicit localization bounds derived from the Bernoulli ODE. To illustrate the applicability of the framework, we present what appears to be the first use of a porous-medium-type diffusion equation in digital image denoising, achieving performance comparable to or better than the Perona--Malik anisotropic diffusion filter, together with a complete Python implementation. The approach extends naturally to models in physics, biology, economics, and engineering, providing concrete predictions across a range of diffusion-driven phenomena.

math.AP

Radial and Non-Radial Solution Structures for Quasilinear Hamilton--Jacobi--Bellman Equations in Bounded Settings

This paper establishes the existence, uniqueness, and global $C^{1,β}$ regularity of positive classical solutions to a class of quasilinear Hamilton--Jacobi--Bellman (HJB) equations with Dirichlet boundary conditions on bounded convex domains. The core technical contribution is a constructive existence proof based on a weighted linear monotone iteration scheme. This scheme's stability and convergence are rigorously established through the construction of adaptive sub- and super-solutions leveraging the torsion function of the domain. Additionally, we provide a complete probabilistic derivation of the quasilinear PDE from the framework of controlled Itô diffusions, formally bridging the gap between stochastic optimal control theory and elliptic regularity analysis. Our results extend beyond the classical quadratic cost regime to the wider class of sub-quadratic growth source terms. Finally, we demonstrate the utility of this theoretical framework through high-precision numerical implementations in two distinct fields: stochastic production planning and nonlinear contrast enhancement in image restoration.

math.AP

Well-Posedness and Monotone Analysis for a Coupled Sublinear Lane--Emden--Fowler System on Bounded Domains

We investigate a coupled system of elliptic equations of Lane--Emden--Fowler type on a bounded domain $Ω\subset \mathbb{R}^n$ ($n \geq 1$) with homogeneous Dirichlet boundary conditions. The system is characterized by sublinear power-law reaction terms $0 < α, β< 1$ and includes a fidelity regularization component. Due to the non-gradient structure of the coupling, we employ the method of sub- and supersolutions and a monotone iteration scheme to establish the existence of positive solutions. We prove that the system admits a unique positive solution $(u,v) \in C^{1,γ}(% \overlineΩ) \times C^{1,γ}(\overlineΩ)$ for some $γ\in (0,1)$, and we demonstrate the continuous dependence of the solution on the data. For the discrete case, we establish the monotone convergence of a fixed-point algorithm by verifying the conditions of Krasnosel'ski\uı's theorem for monotone sub-homogeneous operators. This work provides a rigorous mathematical foundation for coupled reaction-diffusion models where traditional variational minimization is not directly applicable.

math.AP

Well-Posedness and Numerical Approximation of a Class of Nonlocal Elliptic Equations with Gaussian Kernels

This paper investigates the mathematical properties and numerical approximation of a class of nonlocal elliptic partial differential equations of the form \begin{equation*} -Δu + λ\, G(u) = f, \end{equation*} where $Δ$ denotes the Laplacian, $λ> 0$ is a regularization parameter, and $G$ is a nonlocal operator defined by integral convolution with a kernel $K$. We establish the well-posedness of the problem in the Sobolev space $H_0^1(Ω)$ using the Lax--Milgram theorem, providing rigorous proofs for the existence, uniqueness, and positivity of the weak solution under standard assumptions on the kernel $K$ and the source term $f \in L^2(Ω)$. For the numerical treatment, we employ a finite difference discretization for the Laplacian and a Gaussian-based approximation for the nonlocal term. We analyze a fixed-point iterative scheme for solving the discrete system and derive explicit conditions for its convergence and stability. Numerical experiments validate the theoretical results, demonstrating the monotonic decay of the residual and the robustness of the approximation scheme on bounded domains with various padding strategies.

math.AP

New Insights into Population Dynamics from the Continuous McKendrick Model

This article presents a comprehensive study of the continuous McKendrick model, which serves as a foundational framework in population dynamics and epidemiology. The model is formulated through partial differential equations that describe the temporal evolution of the age distribution of a population using continuously defined birth and death rates. In this work, we provide rigorous derivations of the renewal equation, establish the appropriate boundary conditions, and perform a detailed analysis of the survival functions. The central result demonstrates that the population approaches extinction if and only if the net reproduction number $R_{n}$ is strictly less than unity. We present two independent proofs: one based on Laplace transform techniques and Tauberian theorems, and another employing a reformulation as a system of ordinary differential equations with eigenvalue analysis. Additionally, we establish the connection between the deterministic framework and stochastic process formulations, showing that the McKendrick equation emerges as the fluid limit of an individual-based stochastic model.

q-bio.PE

A Nonlinear Logistic Model for Age-Structured Populations: Analysis of Long-Term Dynamics and Equilibria

This paper investigates a nonlinear logistic model for age-structured population dynamics. The model incorporates interdependent fertility and mortality functions within a logistic framework, offering insights into stationary solutions and asymptotic behavior. Theoretical findings establish conditions for the existence and uniqueness of equilibrium solutions and explore long-term population dynamics. This study provides valuable tools for demographic modeling and opens avenues for further mathematical exploration.

math.AP

Existence and large radial solutions for an elliptic system under finite new Keller-Osserman integral conditions

We study the semilinear elliptic system \[ Δu = p(|x|)\,g(v), \qquad Δv = q(|x|)\,f(u), \qquad x \in \mathbb{R}^n,\; n \geq 3, \] under new Keller--Osserman-type integral conditions on the nonlinearities $f,g$ and decay constraints on the radial weights $p,q$. Within this framework we prove: (i) existence of infinitely many entire positive radial solutions for admissible central values; (ii) closedness of the set of all admissible central values; and (iii) largeness (blow-up at infinity) of solutions at boundary points. The analysis combines comparison principles, compactness arguments, and Keller--Osserman transforms, thereby extending classical theory to coupled elliptic systems with general nonlinearities and weights.

math.AP

An Iterative PDE Based Illumination Restoration Scheme for Image Enhancement

We present a novel iterative scheme for restoring uneven illumination in grayscale images. Our approach solves, at each global iteration, a nonlinear elliptic equation for an auxiliary field $u$ and then updates the illumination via an explicit time-marching step driven by the logarithmic potential of $u$. We establish existence and uniqueness of the discrete subproblems, analyze convergence of the fixed point iteration, and demonstrate performance on standard test images, reporting PSNR and SSIM gains over classical methods. Future work will address color coupling and GPU acceleration.

math.AP

Stochastic Production Planning in Manufacturing Systems

We extend the stochastic production planning framework to manufacturing systems, where the set of admissible production configurations is described by a general smooth convex domain $ω$. In our setting, production operations continue as long as the production inventory $y(t)$ remains inside the capacity limits of $ω$ and are halted once the state exits this region, i.e.,% \begin{equation*} τ=\inf \{t>0:\Vert y(t)-x_{0}\Vert >\text{dist}(x_{0},\partial ω)\}. \end{equation*}% The running cost is partitioned into a quadratic production cost $% a(p)=\left\Vert p\right\Vert ^{2}$ and an inventory holding cost modeled by a positive continuous function $b(y)$. We derive the associated Hamilton--Jacobi--Bellman (HJB) equation, verify the supermartingale property of the value function, and characterize the optimal feedback control. Techniques inspired by Lasry, Lions and Alvarez enable us to prove existence and uniqueness within this generalized production planning framework. Numerical experiments and a real-world examples illustrate the practical relevance of our results.

math.OC

Stochastic Production Planning: Optimal Control and Analytical Insights

This study investigates a stochastic production planning problem with a running cost composed of quadratic production costs and inventory-dependent costs. The objective is to minimize the expected cost until production stops when inventory reaches a specified level, subject to a boundary condition. Using probability space and Brownian motion, the Hamilton-Jacobi-Bellman (HJB) equation is derived, and optimal feedback control is obtained. The solution demonstrates desirable monotonicity and convexity properties under specific assumptions. An illustrative example further confirms these results with explicit function properties and a practical application.

math.OC

Image Restoration via Integration of Optimal Control Techniques and the Hamilton-Jacobi-Bellman Equation

In this paper, we propose a novel image restoration framework that integrates optimal control techniques with the Hamilton-Jacobi-Bellman (HJB) equation. Motivated by models from production planning, our method restores degraded images by balancing an intervention cost against a state-dependent penalty that quantifies the loss of critical image information. Under the assumption of radial symmetry, the HJB equation is reduced to an ordinary differential equation and solved via a shooting method, from which the optimal feedback control is derived. Numerical experiments, supported by extensive parameter tuning and quality metrics such as PSNR and SSIM, demonstrate that the proposed framework achieves significant improvement in image quality. The results not only validate the theoretical model but also suggest promising directions for future research in adaptive and hybrid image restoration techniques.

math.AP