arXiv subjects

Shuting Wu

Publications and source records attributed to Shuting Wu.

7 recordsLinked to original sources

arXiv2026

CADMM-Prox: A Bi-level Consensus ADMM for Non-smooth Non-convex Distributed Consensus Optimization

Non-smooth and non-convex optimization problems are pervasive in machine learning, control, and signal processing, due to the need for sparse solutions and the inherently non-convex nature of many objective functions. In this paper, we study non-smooth and non-convex distributed optimization problems. We propose a novel bi-level Consensus Alternating Direction Method of Multipliers (ADMM) algorithm, termed CADMM-Prox. The proposed algorithm integrates classical Consensus ADMM with a proximal mechanism by introducing a sufficiently large proximal term associated with an outer-level variable. Under the mild assumption that the local objective functions are semi-convex, CADMM-Prox is guaranteed to converge globally to a neighborhood of a Clarke stationary point. Numerical experiments on a phase retrieval problem demonstrate that our proposed method exhibits more stable convergence behavior compared with baseline algorithm.

math.OC↗

arXiv2026

A Global Convergence Analysis of Consensus ALADIN for Convex Optimization

Distributed optimization problems are pervasive in machine learning and optimal control. In this paper, we study smooth strongly convex distributed consensus optimization problems. We present a distributed optimization algorithm for consensus problems based on the Consensus Augmented Lagrangian Alternating Direction Inexact Newton (C-ALADIN) framework. Our algorithm uses an auxiliary variable to decide when to update second-order information, enabling curvature exploitation without sacrificing global convergence. This contrasts with existing C-ALADIN methods, which require constant Hessian approximations and thus lose numerical advantages. Under smooth strong convexity, the algorithm converges globally, and the auxiliary variable converges sublinearly. Numerical experiments on logistic regression show that our algorithm outperforms baseline methods that use either fixed or updated Hessian information.

eess.SY↗

arXiv2025

A Time Splitting Based Optimization Method for Nonlinear MHE

Moving Horizon Estimation~(MHE) is essentially an optimization-based approach designed to estimate the states of dynamic systems within a moving time horizon. Traditional MHE solutions become computationally prohibitive due to the \textit{curse of dimensionality} arising from increasing problem complexity and growing length of time horizon. To address this issue, we propose novel computationally efficient algorithms for solving nonlinear MHE problems. Specifically, we first introduce a distributed reformulation utilizing a time-splitting technique. Leveraging this reformulation, we develop the Efficient Gauss-Newton Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) to achieve computational efficiency. Additionally, to accommodate limited computational capabilities inherent in some sub-problem solvers, we propose the Efficient Sensitivity Assisted ALADIN, which enables sub-problems to be solved inexactly without hindering computational efficiency. Furthermore, recognizing scenarios where sub-problem solvers possess no computational power, we propose a Distributed Sequential Quadratic Programming (SQP) that relies solely on first- and second-order information of local objective functions. We demonstrate the performance and advantages of our proposed methods through numerical experiments on differential drive robots case, a practical nonlinear MHE problem. Our results demonstrate that the three proposed algorithms achieve computational efficiency while preserving high accuracy, thereby satisfying the real-time requirements of MHE.

eess.SY↗

arXiv2025

ALADIN-$\beta$: A Distributed Optimization Algorithm for Solving MPCC Problems

Mathematical Programs with Complementarity Constraints (MPCC) are critical in various real-world applications but notoriously challenging due to non-smoothness and degeneracy from complementarity constraints. The $\ell_1$-Exact Penalty-Barrier enhanced \texttt{IPOPT} improves performance and robustness by introducing additional inequality constraints and decision variables. However, this comes at the cost of increased computational complexity due to the higher dimensionality and additional constraints introduced in the centralized formulation. To mitigate this, we propose a distributed structure-splitting reformulation that decomposes these inequality constraints and auxiliary variables into independent sub-problems. Furthermore, we introduce Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN)-$\beta$, a novel approach that integrates the $\ell_1$-Exact Penalty-Barrier method with ALADIN to efficiently solve the distributed reformulation. Numerical experiments demonstrate that even without a globalization strategy, the proposed distributed approach achieves fast convergence while maintaining high precision.

eess.SY↗

arXiv2025

FF7: A Code Package for High-throughput Calculations and Constructing Materials Database

Decades accumulation of theory simulations lead to boom in material database, which combined with machine learning methods has been a valuable driver for the data-intensive material discovery, i.e., the fourth research paradigm. However, construction of segmented databases and data reuse in generic databases with uniform parameters still lack easy-to-use code tools. We herein develop a code package named FF7 (Fast Funnel with 7 modules) to provide command-line based interactive interface for performing customized high-throughput calculations and building your own handy databases. Data correlation studies and material property prediction can progress by built-in installation-free artificial neural network module and various post processing functions are also supported by auxiliary module. This paper shows the usage of FF7 code package and demonstrates its usefulness by example of database driven thermodynamic stability high-throughput calculation and machine learning model for predicting the superconducting critical temperature of clathrate hydrides.

cond-mat.mtrl-sci↗

arXiv2024

High-throughput discovery of robust room-temperature superconductors among complex ternary clathrate hydrides

After the decade-long exhaustive study of binary high-Tc superconducting hydrides, the frontier of this stimulating research field has recently shifted to ternary hydrides with much expanded conformational space in search of coveted room-temperature superconductors. This task, however, presents a formidable challenge due to enormous demands on computational resources. Here, we devise an efficient high-throughput approach using keen material insights and a self-built database to screen for robust ternary hydrides in clathrate structures, which were proven to host highest Tc in binary hydrides, and to estimate Tc by a reliable empirical formula. This approach has made it possible to uncover a diverse set of complex multiple-hydrogen-cage ternary hydrides hosting near or above room-temperature Tc, which are beyond the reach of prevailing structure search methods. This study establishes a distinct paradigm that opens a fresh avenue to enable and accelerate the discovery of promising room-temperature superconductors among unprecedented complex clathrate hydrides.

cond-mat.supr-con↗

arXiv2023

Automatic Debiased Learning from Positive, Unlabeled, and Exposure Data

We address the issue of binary classification from positive and unlabeled data (PU classification) with a selection bias in the positive data. During the observation process, (i) a sample is exposed to a user, (ii) the user then returns the label for the exposed sample, and (iii) we however can only observe the positive samples. Therefore, the positive labels that we observe are a combination of both the exposure and the labeling, which creates a selection bias problem for the observed positive samples. This scenario represents a conceptual framework for many practical applications, such as recommender systems, which we refer to as ``learning from positive, unlabeled, and exposure data'' (PUE classification). To tackle this problem, we initially assume access to data with exposure labels. Then, we propose a method to identify the function of interest using a strong ignorability assumption and develop an ``Automatic Debiased PUE'' (ADPUE) learning method. This algorithm directly debiases the selection bias without requiring intermediate estimates, such as the propensity score, which is necessary for other learning methods. Through experiments, we demonstrate that our approach outperforms traditional PU learning methods on various semi-synthetic datasets.

cs.LG↗