Searcharxiv⌕ Search

arXiv subjects

Liangquan Zhang

Publications and source records attributed to Liangquan Zhang.

At least 19 recordsLinked to original sources
arXiv2026

Closed-loop $α$-Potential Stochastic Differential Games via a BSDE Approach

In this paper, we study the closed-loop $α$-potential stochastic differential game (SDG) problem as a continuation of our prior research on open-loop control (see \cite{GLZ2025}). By utilizing the backward stochastic differential equation (BSDE) approach, we derive a precise estimate for the parameter $α$. Compared to our earlier work \cite{GLZ2025}, this study incorporates both first- and second-order sensitivity state processes, as well as the sensitivity of the control process. A distinguishing feature of this work is that, in the context of $N$-player heterogeneous agent games involving mean-field type interactions, we derive an $N$-uniform upper bound for the minimal potential approximation error. In contrast to the corresponding open-loop estimates, the closed-loop bound contains feedback-induced contributions that need not vanish with $N$. Consequently, our present estimate does not in general guarantee $α\rightarrow 0$.

math.OC↗
arXiv2026

Zero-Noise Limit for High-Dimensional ODE with Measurable Drift

This paper studies the zero-noise limit of high-dimensional small-noise diffusion processes governed by the stochastic differential equation (SDE): \[ dX_{t}^{\varepsilon }=b(X_{t}^{\varepsilon })\,dt+\varepsilon \,dW_{t}, \quad X_{0}^{\varepsilon }=0, \quad \varepsilon >0, \] where drift $b$ is measurable and bounded. The associated ordinary differential equation (ODE) $\dot{x}_{t}=b(x_{t})$ may have multiple Filippov solutions due to lack of Lipschitz continuity, while non-degenerate additive noise ensures unique strong solutions for each $\varepsilon >0$. Integrating the Stroock-Varadhan support theorem, comparison theorem for diffusion processes, law of the iterated logarithm (LIL) for Brownian motion, and Hausdorff dimension from geometric measure theory, we analyze the weak limit distribution $μ^{0}=\lim_{\varepsilon \rightarrow 0}\mathcal{L}(X_{t}^{\varepsilon })$. We find instantaneous escape Filippov solutions dominate the zero-noise limit, with the support of $μ^{0}$ being the closure of points reached by these solutions at fixed $t$ (delayed solutions are geometrically negligible). The comparison theorem verifies uniform weak convergence under small drift perturbations; LIL quantifies $X_{t}^{\varepsilon }$ fluctuations as $\varepsilon \rightarrow 0$; Hausdorff dimension analysis shows the support has dimension strictly less than ambient space dimension $d$, making $μ^{0}$ singular with respect to the Lebesgue measure. The compact support set's structure depends only on drift dynamics and instantaneous escape solutions, not Brownian motion or $d$. Our work unifies probabilistic limit theory, geometric measure theory, ODE non-uniqueness and differential inclusion theory, providing a comprehensive framework for high-dimensional non-unique systems' zero-noise limit and new insights into singular limit distributions in stochastic analysis.

math.PR↗
arXiv2025

BSDE Approach for $α$-Potential Stochastic Differential Games

In this paper, we examine a class of $α$-potential stochastic differential games with random coefficients via the backward stochastic differential equations (BSDEs) approach. Specifically, we show that the first and second order linear derivatives of the objective function for each player can be expressed through the corresponding first and second-order adjoint equations, which leads to rigorous estimates for $α$. We illustrate the dependence of $α$ on game characteristics through detailed analysis of linear-quadratic games, and with common noise.

math.OC↗
arXiv2024

Stochastic Recursive Optimal Control of McKean-Vlasov Type: A Viscosity Solution Approach

In this paper, we study a kind of optimal control problem for forward-backward stochastic differential equations (FBSDEs for short) of McKean--Vlasov type via the dynamic programming principle (DPP for short) motivated by studying the infinite dimensional Hamilton--Jacobi--Bellman (HJB for short) equation derived from the decoupling field of the FBSDEs posed by Carmona and Delarue (\emph{Ann Probab}, 2015, \cite{cd15}). At the beginning, the value function is defined by the solution to the controlled BSDE alluded to earlier. On one hand, we can prove the value function is deterministic function with respect to the initial random variable; On the other hand, we can show that the value function is \emph{law-invariant}, i.e., depending on only via its distribution by virtue of BSDE property. Afterward, utilizing the notion of differentiability with respect to probability measures introduced by P.L. Lions \cite{Lions2012}, we are able to establish a DPP for the value function in the Wasserstein space of probability measures based on the application of BSDE approach, particularly, employing the notion of stochastic \emph{backward semigroups} associated with stochastic optimal control problems and Itô formula along a flow property of the conditional law of the controlled forward state process. We prove that the value function is the unique viscosity solutions of the associated generalized HJB equations in some separable Hilbert space. Finally, as an application, we formulate an optimal control problem for linear stochastic differential equations with quadratic cost functionals of McKean-Vlasov type under nonlinear expectation, $g$-expectation introduced by Peng \cite{Peng04} and derive the optimal feedback control explicitly by means of several groups of Riccati equations.

math.OC↗
arXiv2023

Dynamic Programming for Indefinite Stochastic McKean-Vlasov LQ Control Problem under Input Constraints

In this note, we study a class of indefinite stochastic McKean-Vlasov linear-quadratic (LQ in short) control problem under the control taking nonnegative values. In contrast to the conventional issue, both the classical dynamic programming principle (DPP in short) and the usual Riccati equation approach fail. We tackle these difficulties by extending the state space from $\mathbb{R}$ to probability measure space, afterward derive the the corresponding the infinite dimensional Hamilton--Jacobi--Bellman (HJB in short) equation. The optimal control and value function can be obtained basing on two functions constructed via two groups of novelty ordinary differential equations satisfying the HJB equation mentioned before. As an application, we revisit the mean-variance portfolio selection problems in continuous time under the constraint that short-selling of stocks is prohibited. The investment risk and the capital market line can be captured simultaneously.

math.OC↗
arXiv2021

Global Solutions of Stochastic Stackelberg Differential Games under Convex Control Constraint

This paper is concerned with a Stackelberg stochastic differential game, where the systems are driven by stochastic differential equation (SDE for short), in which the control enters the randomly disturbed coefficients (drift and diffusion). The control region is postulated to be convex. By making use of the first-order adjoint equation (backward stochastic differential equation, BSDE for short), we are able to establish the Pontryagin's maximum principle for the leader's global Stackelberg solution, within adapted open-loop structure and closed-loop memoryless information one, respectively, where the term global indicates that the leader's domination over the entire game duration. Since the follower's adjoint equation turns out to be a BSDE, the leader will be confronted with a control problem where the state equation is a kind of \emph{fully} coupled forward-backward stochastic differential equation (FBSDE for short). As an application, we study a class of linear-quadratic (LQ for short) Stackelberg games in which the control process is constrained in a closed convex subset $Γ$ of full space $\mathbb{R}^{m}$. The state equations are represented by a class of fully coupled FBSDEs with projection operators on $Γ$. By means of monotonicity condition method, the existence and uniqueness of such FBSDEs are obtained. When the control domain is full space, we derive the resulting backward stochastic Riccati equations.

math.OC↗
arXiv2021

A BSDE Approach to Stochastic Differential Games Involving Impulse Controls and HJBI Equation

This paper focuses on zero-sum stochastic differential games in the framework of forward-backward stochastic differential equations on a finite time horizon with both players adopting impulse controls. By means of BSDE methods, in particular that of the notion from Peng's stochastic \textit{% backward semigroups}, we prove a dynamic programming principle for both the upper and the lower value functions of the game. The upper and the lower value functions are then shown to be the unique viscosity solutions of the Hamilton-Jacobi-Bellman-Isaacs equations with a double-obstacle. As a consequence, the uniqueness implies that the upper and lower value functions coincide and the game admits a value.

math.OC↗
arXiv2020

Singular Optimal Controls for Stochastic Recursive Systems under Convex Control Constraint

In this paper, we study two kinds of singular optimal controls (SOCs for short) problems where the systems governed by forward-backward stochastic differential equations (FBSDEs for short), in which the control has two components: the regular control, and the singular one. Both drift and diffusion terms may involve the regular control variable. The regular control domain is postulated to be convex. Under certain assumptions, in the framework of the Malliavin calculus, we derive the pointwise second-order necessary conditions for stochastic SOC in the classical sense. This condition is described by two adjoint processes, a maximum condition on the Hamiltonian supported by an illustrative example. A new necessary condition for optimal singular control is obtained as well. Besides, as a by-product, a verification theorem for SOCs is derived via viscosity solutions without involving any derivatives of the value functions. It is worth pointing out that this theorem has wider applicability than the restrictive classical verification theorems. Finally, we focus on the connection between the maximum principle and the dynamic programming principle for such SOCs problem without the assumption that the value function is smooth enough.

math.OC↗
arXiv2019

Mean Field Game for Linear Quadratic Stochastic Recursive Systems

This paper focuses on linear-quadratic (LQ for short) mean-field games described by forward-backward stochastic differential equations (FBSDEs for short), in which the individual control region is postulated to be convex. The decentralized strategies and consistency condition are represented by a kind of coupled mean-field FBSDEs with projection operators. The well-posedness of consistency condition system is obtained using the monotonicity condition method. The $ε$-Nash equilibrium property is discussed as well.

math.OC↗
arXiv2019

Optimal Control of Markov Regime-Switching Stochastic Recursive Utilities

In this paper, we establish a general stochastic maximum principle for optimal control for systems described by a continuous-time Markov regime-switching stochastic recursive utilities model. The control domain is postulated not to be convex, and the diffusion terms depend on control variables. To this end, we first study a kind of classical forward stochastic optimal control problems. Afterwards, based on previous results, we introduce two groups of new first and second-order adjoint equations. The corresponding variational equations for forward-backward stochastic differential equations are derived. In particular, the generator in the maximum principle contains solutions of second-order adjoint equation which is novel. Some interesting examples are concluded as well.

math.OC↗
arXiv2018

Singular Optimal Controls of Stochastic Recursive Systems and Hamilton-Jacobi-Bellman Inequality

In this paper, we study the optimal singular controls for stochastic recursive systems, in which the control has two components: the regular control, and the singular control. Under certain assumptions, we establish the dynamic programming principle for this kind of optimal singular controls problem, and prove that the value function is a unique viscosity solution of the corresponding Hamilton-Jacobi-Bellman inequality, in a given class of bounded and continuous functions. At last, an example is given for illustration.

math.OC↗
arXiv2015

The Viability Property for Path-dependent SDE under Open Constraints

In this note, we study the viability of a bounded open domain in $\mathbb{R}% ^{n}$ for a process driven by a path-dependent stochastic differential equation with Lipschitz data. We extend an invariant result of Cannarsa, Da. Prato and Frankowska [\textit{Indiana Univ. Math. J.} \textbf{59} (2010) 53-78] to a non-Markovian setting.

math.PR↗
arXiv2015

Necessary Condition for Near Optimal Control of Linear Forward-backward Stochastic Differential Equations

This paper investigates the near optimal control for a kind of linear stochastic control systems governed by the forward backward stochastic differential equations, where both the drift and diffusion terms are allowed to depend on controls and the control domain is not assumed to be convex. In the previous work (Theorem 3.1) of the second and third authors [\textit{% Automatica} \textbf{46} (2010) 397-404], some problem of near optimal control with the control dependent diffusion is addressed and our current paper can be viewed as some direct response to it. The necessary condition of the near-optimality is established within the framework of optimality variational principle developed by Yong [\textit{SIAM J. Control Optim.} \textbf{48} (2010) 4119--4156] and obtained by the convergence technique to treat the optimal control of FBSDEs in unbounded control domains by Wu [% \textit{Automatica} \textbf{49} (2013) 1473--1480]. Some new estimates are given here to handle the near optimality. In addition, an illustrating example is discussed as well.

math.OC↗
arXiv2012

The Relaxed Stochastic Maximum Principle in the Mean-field Singular Controls

In this paper, we study the optimal control system driven by stochastic differential equations (SDEs) of mean-field type, in which the control variable has two components, the first being absolutely continuous and the second singular. On the other hand, the coefficients depend on the state of the solution process as well as of its expected value. Moreover, the cost functional is also of mean field type. This makes the control problem time inconsistent in the sense that the Bellman optimality principle does not hold. Our aim is to derive a stochastic maximum principle of optimal control of Pontriagin type to the class of measure-valued controls.

math.OC↗
arXiv2012

General Doubly Stochastic Maximum Principle and Its Applications to Optimal Control of SPDEs

In this paper, we prove the necessary and sufficient maximum principles (NSMPs in short) for the optimal control of systems described by a quasilinear stochastic heat equation within convex control domains, which all the coefficients contain control variables. For that, the optimal control problem of fully coupled forward-backward doubly stochastic system is studied. We apply our NSMPs to treat a kind of forward-backward doubly stochastic linear quadratic optimal control problems and an example of optimal control of stochastic partial differential equations (SPDEs in short) as well.

math.OC↗
arXiv2012

Stochastic Maximum Principle for Mean-field Controls and Non-Zero Sum Mean-field Game Problems for Forward-Backward Systems

The objective of the present paper is to investigate the solution of fully coupled mean-field forward-backward stochastic differential equations (FBSDEs in short) and to study the stochastic control problems of mean-field type as well as the mean-field stochastic game problems both in which state processes are described as FBSDEs. By combining classical FBSDEs methods introduced by Hu and Peng [Y. Hu, S. Peng, Solution of forward-backward stochastic differential equations, Probab. Theory Relat. Fields 103 (1995)] with specific arguments for fully coupled mean-field FBSDEs, we prove the existence and uniqueness of the solution to this kind of fully coupled mean-field FBSDEs under a certain \textquotedblleft monotonicity" condition. Next, we are interested in optimal control problems for (fully coupled respectively) FBSDEs of mean-field type with a convex control domain. Note that the control problems are time inconsistent in the sense that the Bellman optimality principle does not hold. The stochastic maximum principle (SMP) in integral form for mean-field controls, which is different from the classical one, is derived, specifying the necessary conditions for optimality. Sufficient conditions for the optimality of a control is also obtained under additional assumptions. Then we are concerned the maximum principle for a new class of non-zero sum stochastic differential games. This game system differs from the existing literature in the sense that the game systems here are characterized by (fully coupled respectively) FBSDEs in the mean-field framework. Our paper deduces necessary conditions as well as sufficient conditions in the form of maximum principle for open equilibrium point of this class of games respectively.

math.OC↗
arXiv2012

Stochastic Verification Theorem of Forward-Backward Controlled Systems for Viscosity Solutions

In this paper, we investigate the controlled system described by forward-backward stochastic differential equations with the control contained in drift, diffusion and generator of BSDE. A new verification theorem is derived within the framework of viscosity solutions without involving any derivatives of the value functions. It is worth to pointing out that this theorem has wider applicability than the restrictive classical verification theorems. As a relevant problem, the optimal stochastic feedback controls for forward-backward system are discussed as well.

math.OC↗