arXiv subjects

Laigang Guo

Publications and source records attributed to Laigang Guo.

10 recordsLinked to original sources

arXiv2026

An Efficient Symbolic Algorithm for Computing Lyapunov Constants in Planar Switching Systems

Lyapunov constants are a crucial tool for analyzing bifurcation behavior in switching systems. This paper proposes an efficient algebraic symbolic algorithm for computing Lyapunov constants in planar switching systems. By mapping the vector fields into the complex domain, we construct a normal form algorithm based on Laurent poly?nomials. The classical Poincaré map method requires repeated symbolic integration, which may lead to substantial computational difficulties at high orders. Our ap?proach improves computational efficiency by replacing continuous integration with direct algebraic operations. We apply this approach to investigate two switching models. For a generalized switching quartic Liénard system, the algebraic method extracts the center conditions and proves the existence of 10 small-amplitude limit cycles, establishing a new lower bound for its cyclicity. Furthermore, a physically motivated circuit-level modification of the Alpazur oscillator is investigated. We show that at most one small-amplitude limit cycle can bifurcate from the center and that this upper bound is attainable, illustrating the applicability of the proposed algorithm to a practical switching circuit. This application shows that our method provides a systematic framework for analyzing and interpreting complex dynamical phenomena arising in practical piecewise smooth systems.

math.DS↗

arXiv2026

An Algebraic Method for Full-Rank Characterization in Binary Linear Coding

In this paper, we develop a characteristic set (CS)-based method for deriving full-rank equivalence conditions of symbolic matrices over the binary field. Such full-rank conditions are of fundamental importance for many linear coding problems in communication and information theory. Building on the developed CS-based method, we present an algorithm called Binary Characteristic Set for Full Rank (BCSFR), which efficiently derives the full-rank equivalence conditions as the zeros of a series of characteristic sets. In other words, the BCSFR algorithm can characterize all feasible linear coding schemes for certain linear coding problems (e.g., linear network coding and distributed storage coding), where full-rank constraints are imposed on several symbolic matrices to guarantee decodability or other properties of the codes. The derived equivalence conditions can be used to simplify the optimization of coding schemes, since the intractable full-rank constraints in the optimization problem are explicitly characterized by simple triangular-form equality constraints.

cs.IT↗

arXiv2026

Characterizations of Conditional Mutual Independence: Equivalence and Implication

Conditional independence, and more generally conditional mutual independence, are central notions in probability theory. In their general forms, they include functional dependence as a special case. In this paper, we tackle two fundamental problems related to conditional mutual independence. Let $K$ and $K'$ be two conditional mutual independncies (CMIs) defined on a finite set of discrete random variables. We have obtained a necessary and sufficient condition for i) $K$ is equivalent to $K'$; ii) $K$ implies $K'$. These characterizations are in terms of a canonical form introduced for conditional mutual independence.

math.PR↗

arXiv2025

On the Complete Monotonicity of Rényi Entropy

In this paper, we investigate the complete monotonicity of Rényi entropy along the heat flow. We confirm this property for the order of derivative up to $4$, when the order of Rényi entropy is in certain regimes. We also investigate concavity of Rényi entropy power and the complete monotonicity of Tsallis entropy. We recover and slightly extend Hung's result on the fourth-order derivative of the Tsallis entropy, and observe that the complete monotonicity holds for Tsallis entropy of order $2$, which is equivalent to that the noise stability with respect to the heat semigroup is completely monotone. Based on this observation, we conjecture that the complete monotonicity holds for Tsallis entropy of all orders $α\in(1,2)$. Our proofs in this paper are based on the techniques of integration-by-parts, sum-of-squares, and curve-fitting.

cs.IT↗

arXiv2024

Sliding Secure Symmetric Multilevel Diversity Coding

Symmetric multilevel diversity coding (SMDC) is a source coding problem where the independent sources are ordered according to their importance. It was shown that separately encoding independent sources (referred to as ``\textit{superposition coding}") is optimal. In this paper, we consider an $(L,s)$ \textit{sliding secure} SMDC problem with security priority, where each source $X_α~(s\leq α\leq L)$ is kept perfectly secure if no more than $α-s$ encoders are accessible. The reconstruction requirements of the $L$ sources are the same as classical SMDC. A special case of an $(L,s)$ sliding secure SMDC problem that the first $s-1$ sources are constants is called the $(L,s)$ \textit{multilevel secret sharing} problem. For $s=1$, the two problems coincide, and we show that superposition coding is optimal. The rate regions for the $(3,2)$ problems are characterized. It is shown that superposition coding is suboptimal for both problems. The main idea that joint encoding can reduce coding rates is that we can use the previous source $X_{α-1}$ as the secret key of $X_α$. Based on this idea, we propose a coding scheme that achieves the minimum sum rate of the general $(L,s)$ multilevel secret sharing problem. Moreover, superposition coding of the $s$ sets of sources $X_1$, $X_2$, $\cdots$, $X_{s-1}$, $(X_s, X_{s+1}, \cdots, X_L)$ achieves the minimum sum rate of the general sliding secure SMDC problem.

cs.IT↗

arXiv2024

Proving Information Inequalities by Gaussian Elimination

The proof of information inequalities and identities under linear constraints on the information measures is an important problem in information theory. For this purpose, ITIP and other variant algorithms have been developed and implemented, which are all based on solving a linear program (LP). In this paper, we develop a method with symbolic computation. Compared with the known methods, our approach can completely avoids the use of linear programming which may cause numerical errors. Our procedures are also more efficient computationally.

cs.IT↗

arXiv2022

Proving Information Inequalities and Identities with Symbolic Computation

Proving linear inequalities and identities of Shannon's information measures, possibly with linear constraints on the information measures, is an important problem in information theory. For this purpose, ITIP and other variant algorithms have been developed and implemented, which are all based on solving a linear program (LP). In particular, an identity $f = 0$ is verified by solving two LPs, one for $f \ge 0$ and one for $f \le 0$. In this paper, we develop a set of algorithms that can be implemented by symbolic computation. Based on these algorithms, procedures for verifying linear information inequalities and identities are devised. Compared with LP-based algorithms, our procedures can produce analytical proofs that are both human-verifiable and free of numerical errors. Our procedures are also more efficient computationally. For constrained inequalities, by taking advantage of the algebraic structure of the problem, the size of the LP that needs to be solved can be significantly reduced. For identities, instead of solving two LPs, the identity can be verified directly with very little computation.

cs.IT↗

arXiv2021

A Generalization of the Concavity of Rényi Entropy Powe

Recently, Savaré-Toscani proved that the Rényi entropy power of general probability densities solving the $p$-nonlinear heat equation in $\mathbb{R}^n$ is always a concave function of time, which extends Costa's concavity inequality for Shannon's entropy power to Rényi entropies. In this paper, we give a generalization of Savaré-Toscani's result by giving a class of sufficient conditions of the parameters under which the concavity of the Rényi entropy power is still valid. These conditions are quite general and include the parameter range given by Savaré-Toscani as special cases. Also, the conditions are obtained with a systematical approach.

cs.IT↗

arXiv2020

Lower Bound on Derivatives of Costa's Differential Entropy

Several conjectures concern the lower bound for the differential entropy $H(X_t)$ of an $n$-dimensional random vector $X_t$ introduced by Costa. Cheng and Geng conjectured that $H(X_t)$ is completely monotone, that is, $C_1(m,n): (-1)^{m+1}(d^m/d^m t)H(X_t)\ge0$. McKean conjectured that Gaussian $X_{Gt}$ achieves the minimum of $(-1)^{m+1}(d^m/d^m t)H(X_t)$ under certain conditions, that is, $C_2(m,n): (-1)^{m+1}(d^m/d^m t)H(X_t)\ge(-1)^{m+1}(d^m/d^m t)H(X_{Gt})$. McKean's conjecture was only considered in the univariate case before: $C_2(1,1)$ and $C_2(2,1)$ were proved by McKean and $C_2(i,1),i=3,4,5$ were proved by Zhang-Anantharam-Geng under the log-concave condition. In this paper, we prove $C_2(1,n)$, $C_2(2,n)$ and observe that McKean's conjecture might not be true for $n>1$ and $m>2$. We further propose a weaker version $C_3(m,n): (-1)^{m+1}(d^m/d^m t)H(X_t)\ge(-1)^{m+1}\frac{1}{n}(d^m/d^m t)H(X_{Gt})$ and prove $C_3(3,2)$, $C_3(3,3)$, $C_3(3,4)$, $C_3(4,2)$ under the log-concave condition. A systematical procedure to prove $C_l(m,n)$ is proposed based on semidefinite programming and the results mentioned above are proved using this procedure.

cs.IT↗

arXiv2020

Prove Costa's Entropy Power Inequality and High Order Inequality for Differential Entropy with Semidefinite Programming

Costa's entropy power inequality is an important generalization of Shannon's entropy power inequality. Related with Costa's entropy power inequality and a conjecture proposed by McKean in 1966, Cheng-Geng recently conjectured that $D(m,n): (-1)^{m+1}(\partial^m/\partial^m t)H(X_t)\ge0$, where $X_t$ is the $n$-dimensional random variable in Costa's entropy power inequality and $H(X_t)$ the differential entropy of $X_t$. $D(1,n)$ and $D(2,n)$ were proved by Costa as consequences of Costa's entropy power inequality. Cheng-Geng proved $D(3,1)$ and $D(4,1)$. In this paper, we propose a systematical procedure to prove $D(m,n)$ and Costa's entropy power inequality based on semidefinite programming. Using software packages based on this procedure, we prove $D(3,n)$ for $n=2,3,4$ and give a new proof for Costa's entropy power inequality. We also show that with the currently known constraints, $D(5,1)$ and $D(4,2)$ cannot be proved with the procedure.

math.PR↗