arXiv subjects

Jianzheng Wang

Publications and source records attributed to Jianzheng Wang.

14 recordsLinked to original sources

arXiv2026

A Geometric-Transformation Feature-Adaptive Manifold Restoration Method for Open-Vocabulary Semantic Segmentation of Remote Sensing Images

The semantic information of objects in remote sensing images is typically invariant to geometric transformations from the dihedral group D4. However, SAM3-based open-vocabulary semantic segmentation (OVSS) methods often exhibit inconsistent responses to different geometric transformations. To exploit this property and improve the stability of OVSS for remote sensing images, we propose a feature-adaptive manifold repair method based on dihedral-group geometric transformations. First, we introduce multi-scale harmonic-guided D4 view selection (MH-D4VS) to select complementary candidate views from a set of geometrically transformed views. Next, we propose original-view-anchored adaptive manifold repair (OAMR), which uses the original view as an anchor and reliable cross-view information to selectively repair locally unreliable visual features. Finally, we develop pixel decoder test-time adaptation (PD-TTA) for SAM3, which fine-tunes only the parameters of the GroupNorm layers online during inference, thereby enhancing the model's ability to adapt to sample-level distribution shifts. Experimental results show that the proposed method achieves an average mIoU of 55.6% across eight remote sensing semantic segmentation benchmarks and delivers consistent performance improvements under different SAM3-based inference frameworks.

cs.CV↗

arXiv2026

Random Reshuffling-Based Distributed Nash Equilibrium Seeking

This paper studies random reshuffling (RR)-based distributed Nash equilibrium seeking for noncooperative games. The game is motivated as a sample-average approximation of an underlying expected-value stochastic game, while the algorithmic focus is placed on the resulting finite-sum equilibrium problem. Unlike existing distributed stochastic Nash equilibrium methods that mainly rely on with-replacement sampling, the proposed approach incorporates without-replacement component updates into equilibrium computation over networks. We first consider a full-information benchmark, for which an intermediate reference trajectory and a shuffling variance are introduced to characterize the epoch-wise dynamics induced by RR. The method is then extended to the more practical partial-decision-information setting, where each player updates its action using local estimates of the joint action profile. For the full-information case, a descent-type bound is established for the RR iterates. For the distributed partial-decision-information case, it is shown that, under constant parameters, the proposed algorithm converges linearly to a neighborhood of the Nash equilibrium, while under diminishing parameters, it converges exactly to the Nash equilibrium almost surely and in mean square. Numerical experiments on an EV charging game and a nonquadratic edge resource admission game demonstrate that RR consistently outperforms the conventional with-replacement SGD baseline in both steady-state accuracy and long-horizon performance.

math.OC↗

arXiv2026

Median-of-Means for Nash Equilibrium Seeking in Heavy-Tailed Games

This paper studies the Nash equilibrium seeking problem for stochastic games under heavy-tailed noise. The gradient noise is considered to have a finite $δ$-th moment ($1<δ\le 2$), which generalizes the Gaussian noise and covers cases with infinite variance. In this work, we employ the classic method Median-of-Means (MoM) in robust estimation. MoM works by dividing samples into blocks, taking the average of each block, and then taking the median of these block averages, achieving a breakdown point of up to $1/2$. This makes the final estimate reliable even when some samples are very noisy or wrong, and thus is effective to handle the heavy-tailed noise. The method also naturally defends against malicious gradient attacks. Compared with gradient clipping, which is the most popular method to deal with the heavy-tailed noise, MoM requires no preset clipping threshold and is insensitive to the tail behavior of the noise. Under standard assumptions, we prove the almost sure convergence of the algorithm and derive its almost sure convergence rate. To address the systematic bias caused by asymmetric noise, we further design an online bias correction strategy. Simulation results show the effectiveness and efficiency of the proposed algorithms.

math.OC↗

arXiv2026

Spatial-Regularization-Aware Dual-Branch Collaborative Inference for Training-Free OVSS in Remote Sensing Imagery

High-resolution remote sensing images contain densely distributed objects with pronounced scale variations and complex boundaries, which impose higher demands on both the geometric localization and semantic prediction capabilities of semantic segmentation models. Existing training-free open-vocabulary semantic segmentation (OVSS) methods typically fuse Contrastive Language-Image Pretraining (CLIP) and vision foundation models (VFMs) using one-way injection and shallow post-processing strategies, making it difficult to satisfy these requirements. To address this issue, we propose a spatial-regularization-aware dual-branch collaborative inference framework for training-free OVSS, termed SDCI. First, during feature encoding, SDCI introduces a cross-model attention fusion (CAF) module, which guides collaborative inference by injecting self-attention maps into each other. Second, we propose a bidirectional cross-graph diffusion refinement (BCDR) module that enhances the reliability of dual-branch segmentation scores through iterative random-walk diffusion. Finally, we incorporate low-level superpixel structures and develop a convex-optimization-based superpixel collaborative prediction (CSCP) mechanism to further refine object boundaries. Experiments on multiple remote sensing semantic segmentation benchmarks demonstrate that our method achieves better performance than existing approaches. Our code is available at https://github.com/yu-ni1989/SDCI.

cs.CV↗

arXiv2025

Distributed Stochastic Optimization for Non-Smooth and Weakly Convex Problems under Heavy-Tailed Noise

In existing distributed stochastic optimization studies, it is usually assumed that the gradient noise has a bounded variance. However, recent research shows that the heavy-tailed noise, which allows an unbounded variance, is closer to practical scenarios in many tasks. Under heavy-tailed noise, traditional optimization methods, such as stochastic gradient descent, may have poor performance and even diverge. Thus, it is of great importance to study distributed stochastic optimization algorithms applicable to the heavy-tailed noise scenario. However, most of the existing distributed algorithms under heavy-tailed noise are developed for convex and smooth problems, which limits their applications. This paper proposes a clipping-based distributed stochastic algorithm under heavy-tailed noise that is suitable for non-smooth and weakly convex problems. The convergence of the proposed algorithm is proven, and the conditions on the parameters are given. A numerical experiment is conducted to demonstrate the effectiveness of the proposed algorithm.

math.OC↗

arXiv2024

Stability Analysis of Distributed Estimators for Large-Scale Interconnected Systems: Time-Varying and Time-Invariant Cases

This paper studies a distributed estimation problem for time-varying/time-invariant large-scale interconnected systems (LISs). A fully distributed estimator is presented by recursively solving a distributed modified Riccati equation (DMRE) with decoupling variables. By partitioning the LIS based on the transition matrix's block structure, it turns out that the stability of the subsystem is independent of the global LIS if the decoupling variable is selected as the number of out-neighbors. Additionally, it is revealed that any LIS can be equivalently represented by a Markov system. Based on this insight, we show that the stability decoupling above can also be achieved if the decoupling variable equals the number of in-neighbors. Then, the distributed estimator is proved to be stable if the DMRE remains uniformly bounded. When the LIS is considered time-invariant, and by analyzing the spectral radius of a linear operator, it is proved that the DMRE is uniformly bounded if and only if a linear matrix inequality is feasible. Based on the boundedness result, we also show that the distributed estimator converges to a unique steady state for any initial condition. Finally, simulations verify the effectiveness of the proposed methods.

eess.SY↗

arXiv2024

Intermittent Encryption Strategies for Anti-Eavesdropping Estimation

In this paper, an anti-eavesdropping estimation problem is investigated. A linear encryption scheme is utilized, which first linearly transforms innovation via an encryption matrix and then encrypts some components of the transformed innovation. To reduce the computation and energy resources consumed by the linear encryption scheme, both stochastic and deterministic intermittent strategies which perform the linear encryption scheme only at partial moments are developed. When the system is stable, it is shown that the mean squared error (MSE) of the eavesdropper converges under any stochastic or deterministic intermittent strategy. Also, an analytical encryption matrix that maximizes the steady-state of the MSE is designed. When the system is unstable, the eavesdropper's MSE can be unbounded with arbitrary positive encryption probabilities and decision functions if encryption matrices are chosen appropriately. Then, the relationship between the aforementioned encryption parameters and the eavesdropper's MSE is analyzed. Moreover, a single intermittent strategy which only encrypts one message is discussed. This strategy can be unavailable for stable systems, but can make the eavesdropper's MSE unbounded in unstable systems for the encrypted message satisfies a linear matrix inequality (LMI) condition. The effectiveness of the proposed methods is verified in the simulation.

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arXiv2023

Duality-Based Distributed Optimization With Communication Delays in Multi-Cluster Networks

In this work, we consider solving a distributed optimization problem (DOP) in a multi-agent network with multiple agent clusters. In each cluster, the agents manage separable cost functions composed of possibly non-smooth components and aim to achieve an agreement on a common decision of the cluster. The global cost function is considered as the sum of the individual cost functions associated with affine coupling constraints on the clusters' decisions. To solve this problem, the dual problem is formulated by the concept of Fenchel conjugate. Then an asynchronous distributed dual proximal gradient (Asyn-DDPG) algorithm is proposed based on a cluster-based partial and mixed consensus protocol, by which the agents are only required to communicate with their neighbors with communication delays. An ergodic convergence result is provided, and the feasibility of the proposed algorithm is verified by solving a social welfare optimization problem in the simulation.

math.OC↗

arXiv2022

Social Profit Optimization with Demand Response Management in Electricity Market: A Multi-timescale Leader-following Approach

In the electricity market, it is quite common that the market participants make "selfish" strategies to harvest the maximum profits for themselves, which may cause the social benefit loss and impair the sustainability of the society in the long term. Regarding this issue, in this work, we will discuss how the social profit can be improved through strategic demand response (DR) management. Specifically, we explore two interaction mechanisms in the market: Nash equilibrium (NE) and Stackelberg equilibrium (SE) among utility companies (UCs) and user-UC interactions, respectively. At the user side, each user determines the optimal energy-purchasing strategy to maximize its own profit. At the UC side, a governmental UC (g-UC) is considered, who aims to optimize the social profit of the market. Meanwhile, normal UCs play games to maximize their own profits. As a result, a basic leader-following problem among the UCs is formulated under the coordination of the independent system operator (ISO). Moreover, by using our proposed demand function amelioration (DFA) strategy, a multi-timescale leader-following problem is formulated. In this case, the maximal market efficiency can be achieved without changing the "selfish instinct" of normal UCs. In addition, by considering the local constraints for the UCs, two projection-based pricing algorithms are proposed for UCs, which can provide approximate optimal solutions for the resulting non-convex social profit optimization problems. The feasibility of the proposed algorithms is verified by using the concept of price of anarchy (PoA) in a multi-UC multi-user market model in the simulation.

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arXiv2022

Distributed Optimization with Coupling Constraints Based on Dual Proximal Gradient Method in Multi-Agent Networks

In this paper, we aim to solve a distributed optimization problem with affine coupling constraints in a multi-agent network, where the cost function of the agents is composed of smooth and possibly non-smooth parts. To solve this problem, we resort to the dual problem by deriving the Fenchel conjugate, resulting in a consensus-based constrained optimization problem. Then, we propose a distributed dual proximal gradient algorithm, where the agents make decisions based on the information of immediate neighbors. Provided that the non-smooth parts in the primal cost functions are with some simple structures, we only need to update dual variables by some simple operations, by which the overall computational complexity can be reduced. An ergodic convergence rate of the proposed algorithm is derived and the feasibility is numerically verified by solving a social welfare optimization problem in the electricity market.

math.OC↗

arXiv2022

Distributed Optimization with Coupling Constraints in Multi-Cluster Networks Based on Dual Proximal Gradient Method

In this work, we consider solving a distributed optimization problem in a multi-agent network with multiple clusters. In each cluster, the involved agents cooperatively optimize a separable composite function with a common decision variable. Meanwhile, a global cost function of the whole network is considered associated with an affine coupling constraint across the clusters. To solve this problem, we propose a cluster-based dual proximal gradient algorithm by resorting to the dual problem, where the global cost function is optimized when the agents in each cluster achieve an agreement on the optimal strategy and the global coupling constraint is satisfied. In addition, the proposed algorithm allows the agents to only communicate with their immediate neighbors. The computational complexity of the proposed algorithm with simple-structured cost functions is discussed and an ergodic convergence with rate O(1/T) is guaranteed (T is the index of iterations).

math.OC↗

arXiv2021

Composite Optimization with Coupling Constraints via Dual Proximal Gradient Method with Applications to Asynchronous Networks

In this paper, we consider solving a composite optimization problem with coupling constraints in a multi-agent network based on proximal gradient method. In this problem, all the agents jointly minimize the sum of individual cost functions composed of smooth and possibly non-smooth parts. To this end, we derive the dual problem by the concept of Fenchel conjugate, which gives rise to the dual proximal gradient algorithm by allowing for the asymmetric individual interpretations of the global constraints. Then, an asynchronous dual proximal gradient algorithm is proposed for the asynchronous networks with heterogenous step-sizes and communication delays. For both the two algorithms, if the non-smooth parts of the objective functions are simple-structured, we only need to update dual variables by some simple operations, accounting for the reduction of the overall computational complexity. Analytical convergence rate of the proposed algorithms is derived and their efficacy is verified by solving a social welfare optimization problem of electricity market in the numerical simulation.

math.OC↗

arXiv2021

Composite Optimization with Coupling Constraints via Penalized Proximal Gradient Method in Partially Asynchronous Networks

In this paper, we consider a composite optimization problem with linear coupling constraints in a multi-agent network. In this problem, all the agents jointly optimize a global composite cost function which is the linear sum of individual cost functions composed of both smooth and non-smooth components. To solve this problem, we propose an asynchronous penalized proximal gradient (Asyn-PPG) algorithm, a variant of classical proximal gradient method, by considering the asynchronous update instants of the agents and communication delays in the network. Specifically, we consider a slot-based asynchronous network (SAN), where the whole time domain is split into sequential time slots and each agent is permitted to make multiple updates during a slot by accessing the historical state information of others. Moreover, we consider a set of global linear constraints and impose some violation penalties on the updating algorithms. By the Asyn-PPG algorithm, we will show that a periodically convergence with rate O(1/K) (K is the index of time slots) can be guaranteed if the coefficient of the penalties for all agents is synchronized at the end of the time slots and the step-size of the Asyn-PPG algorithm is properly determined. The feasibility of the proposed algorithm is verified by solving a consensus based distributed LASSO problem and a social welfare optimization problem in the electricity market respectively.

math.OC↗

arXiv2021

Social Cost Optimization for Prosumer Community with Two Price-Package Incentives in Two-Settlement Based Electricity Market

In this paper, we consider a future electricity market consisting of aggregated energy prosumers, who are equipped with local wind power plants (WPPs) to support (part of) their energy demands and can also trade energy with day-ahead market (DAM) and energy balancing market (EBM). In addition, an energy aggregator (EA) is established, who can provide the trading gateways between prosumers and the markets. The EA is responsible for making pricing strategies on the prosumers to influence their trading behaviours such that the social benefit of the prosumer community is improved. Specifically, two price packages are provided by the EA: wholesale price (WP) package and lump-sum (LS) package, which can be flexibly selected by prosumers based on their own preferences. Analytical energy-trading strategies will be derived for WP prosumers and LS prosumers based on non-cooperative games and Nash resource allocation strategies, respectively. In this work, a social cost optimization problem will be formulated for the EA, where the detailed WP/LS selection plans are unknown in advance. Consequently, a stochastic Stackelberg game between prosumers and the EA is formulated, and a two-level stochastic convex programming algorithm is proposed to minimize the expectation of the social cost. The performance of the proposed algorithm is demonstrated with a two-settlement based market model in the simulation.

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