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Yachen Wang

Publications and source records attributed to Yachen Wang.

5 recordsLinked to original sources

Existence thresholds and limit profiles of ground states for lower critical Choquard equations with general nonlinearities

In this paper, we study the existence, non-existence and asymptotic behavior of positive ground states for the nonlinear Choquard equation: \begin{equation}\label{0.1} -\Delta u+\varepsilon u=\big(I_{\alpha}\ast F(u)\big)F'(u),\quad u\in H^1(\mathbb R^N), \end{equation} where $F(u)=|u|^{\frac{N+\alpha}{N}}+G(u)$ with $G(u)=\int_0^ug(s)ds$, $N\geq3$ is an integer, $I_{\alpha}$ is the Riesz potential of order $\alpha\in(0,N)$ and $\varepsilon>0$ is a frequency parameter. Under some mild subcritical growth assumptions on $g\in C([0,\infty), [0,\infty))$, we establish a sharp threshold result for the existence of ground states, and an asymptotic characterization of the ground state solutions as $\varepsilon\to 0$. In particular, if $g(s)\sim s^{q-1}$ as $s\to 0$ for some $q\in (\frac{N+\alpha}{N}, \frac{N+\alpha}{N-2})$, then if $q<\frac{N+\alpha+4}{N}$, \eqref{0.1} admits a ground state for all $\varepsilon>0$, and if $q\ge \frac{N+\alpha+4}{N}$, then a threshold phenomena occur: there exists $\varepsilon_q>0$ such that \eqref{0.1} has no ground state for $\varepsilon\in (0,\varepsilon_q)$ and admits a ground state for $\varepsilon>\varepsilon_q$. If $g(s)\simeq as^{q-1}$ as $s\to 0$ for some $a>0$ and $q\in (\frac{N+\alpha}{N}, \min\{\frac{N+\alpha}{N-2}, \frac{N+\alpha+4}{N}\})$, we show that as $\varepsilon \to 0$, the ground state solutions of \eqref{0.1}, after a suitable rescaling, converges in $H^1(\mathbb R^N)$ to a particular solution of the Hardy-Littlewood-Sobolev critical equation $u=\frac{N+\alpha}{N}(I_{\alpha}*|u|^{\frac{N+\alpha}{N}})|u|^{\frac{N+\alpha}{N}-2}u$. It turns out that the limit profiles are determined solely by the locations of $(a,q)$ in $(0,+\infty)\times (\frac{N+\alpha}{N}, \min\{\frac{N+\alpha}{N-2}, \frac{N+\alpha+4}{N}\})$. We also establish a novel sharp asymptotic characterization of such a rescaling.

math.AP

From Dynamic to Lexical: A Comparative Exploration of Scoping Rules in SAS and R

Variable scoping dictates how and where variables are accessible within programming languages, playing a crucial role in code efficiency and organization. This paper examines the distinct scoping rules in SAS and R, focusing on SAS's dynamic scoping and R's lexical scoping. In SAS, dynamic scoping utilizes symbol tables, resolving variables at runtime by dynamically searching through active macro layers. R, in contrast, employs lexical scoping, using environments to resolve variables based on the structure in which functions are defined. Illustrative examples highlight the differences between these scoping strategies, showcasing their impact on code behavior. Additionally, the paper outlines methods for inspecting variables in SAS's symbol tables and R's environments, offering practical insights for debugging and optimization. Strategies for controlling variable scope in both languages are discussed, enhancing code precision and reliability. This exploration equips programmers with critical understanding to optimize variable management, improving their programming practices in SAS and R.

cs.PL

Lazy Evaluation: A Comparative Analysis of SAS MACROs and R Functions

Lazy evaluation is a powerful technique that can optimize code execution by deferring evaluations until their results are required, thus enhancing efficiency. In most modern programming languages, like R, lazy evaluation is commonly applied to function arguments. However, the application of lazy evaluation in SAS has not been extensively explored. This paper focuses on the mechanisms of lazy evaluation in SAS MACROs and R functions, offering a comparative analysis of the underlying principles that drive these processes. R's lazy evaluation is driven by a data structure called Promise, which postpones evaluation and does not occupy memory until the value is needed, utilizing a call-by-need strategy. SAS, on the other hand, achieves lazy evaluation through its symbol tables, employing memory to store parameters, and operates on a call-by-name basis. These discrepancies in lazy evaluation strategies can notably impact the results of R functions and SAS MACROs. By examining these distinct approaches, the paper illuminates the impact of lazy evaluation on programming efficiency, supported by illustrative examples. As the shift from SAS to R becomes increasingly prevalent in the pharmaceutical industry, understanding these techniques enables programmers to optimize their code for greater efficacy. This exploration serves as a guide to enhance programming capabilities and performance in both languages.

cs.PL

Limit Theorems for Network Data without Metric Structure

This paper develops limit theorems for random variables with network dependence, without requiring the individuals in the network to be located in a Euclidean or metric space. This distinguishes our approach from most existing limit theorems in network statistics and econometrics, which are based on weak dependence concepts such as strong mixing, near-epoch dependence, or $\psi$-dependence. All these weak dependence concepts presuppose an underlying metric. By relaxing the assumption of an underlying metric space, our theorems can be applied to a broader range of network data, including financial and social networks. To derive the limit theorems, we generalize the concept of functional dependence (also known as physical dependence) from time series to random variables with network dependence. Using this framework, we establish several inequalities, a law of large numbers, and central limit theorems. Furthermore, we demonstrate the verifiability of our high-level conditions by deriving primitive sufficient conditions for spatial autoregressive models, which are widely used in network data analysis.

econ.EM

Asymptotic profiles for Choquard equations with general critical nonlinearities

In this paper, we study asymptotic behavior of positive ground state solutions for the nonlinear Choquard equation: \begin{equation}\label{0.1} -Δu+\varepsilon u=\big(I_α\ast F(u)\big)F'(u),\quad u\in H^1(\mathbb R^N), \end{equation} where $F(u)=|u|^{\frac{N+α}{N-2}}+G(u)$, $N\geq3$ is an integer, $I_α$ is the Riesz potential of order $α\in(0,N)$, and $\varepsilon>0$ is a parameter. Under some mild subcritical growth assumptions on $G(u)$, we show that as $\varepsilon \to \infty$, the ground state solutions of \eqref{0.1}, after a suitable rescaling, converge to a particular solution of the critical Choquard equation $-Δu=\frac{N+α}{N-2}(I_α*|u|^{\frac{N+α}{N-2}})|u|^{\frac{N+α}{N-2}-2}u$. We establish a novel sharp asymptotic characterisation of such a rescaling, which depends in a non-trivial way on the asymptotic behavior of $G(u)$ at infinity and the space dimension $N=3$, $N=4$ or $N\geq5$.

math.AP