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A Heuristic Alternating Direction Method of Multipliers Framework for Distributed and Centralized Tree-Constrained Optimization: Applications to Hop-Constrained Spanning Tree Multicommodity Flow Design

This paper proposes centralized and distributed heuristic ADMM frameworks that combine continuous relaxation with efficient tree-projection subproblems to solve large-scale nonconvex multicommodity flow design problems under spanning tree and hop-constraint requirements, yielding near-optimal solutions.

Yacine MokhtariTue, 10 Ma🔢 math

Global-in-time optimal control of stochastic third-grade fluids with additive noise

This paper establishes the global-in-time well-posedness of stochastic third-grade fluid equations on a two-dimensional torus with additive noise and proves the existence, uniqueness, and first-order optimality conditions for a velocity tracking control problem by transforming the system into an equivalent pathwise deterministic one.

Kush Kinra, Fernanda CiprianoTue, 10 Ma🔢 math

An adaptive proximal safeguarded augmented Lagrangian method for nonsmooth DC problems with convex constraints

This paper presents an adaptive proximal safeguarded augmented Lagrangian method for solving nonsmooth difference-of-convex (DC) optimization problems with convex constraints, proving convergence to a generalized KKT point under a modified Slater condition and validating the approach through numerical experiments.

Christian Kanzow, Tanja NederTue, 10 Ma🔢 math

Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimization

This paper proposes a unified deep meta-learning framework that unrolls an adaptive optimization algorithm to jointly accelerate multi-coil MRI reconstruction and cross-modality synthesis, demonstrating superior generalization and image quality under aggressive undersampling and domain shifts compared to conventional supervised methods.

Merham Fouladvand, Peuroly BatraTue, 10 Ma🔢 math

Adaptive Replication Strategies in Trust-Region-Based Bayesian Optimization of Stochastic Functions

This paper introduces an adaptive replication strategy within a trust-region Bayesian optimization framework that dynamically allocates repeated evaluations to effectively manage high-variance stochastic functions, significantly improving both solution accuracy and computational efficiency compared to baseline methods.

Mickael Binois (ACUMES), Jeffrey Larson (ANL)Tue, 10 Ma🔢 math

A general approach to distributed operator splitting

This paper introduces a general forward-backward splitting framework utilizing coefficient matrices to solve monotone inclusion problems with non-cocoercive operators, unifying existing algorithms while enabling new flexible and distributed implementations.

Minh N. Dao, Matthew K. Tam, Thang D. TruongTue, 10 Ma🔢 math

An Operator Splitting Method for Large-Scale CVaR-Constrained Quadratic Programs

This paper introduces CVQP, an open-source operator splitting method that efficiently solves large-scale quadratic programs with Conditional Value-at-Risk (CVaR) constraints by combining a specialized $O(m\log m)$ projection algorithm with parallel computations, achieving performance orders of magnitude faster than general-purpose solvers for problems involving millions of scenarios.

Eric Luxenberg, David Pérez-Piñeiro, Steven Diamond, Stephen BoydTue, 10 Ma🔢 math

Computing adjoint mismatch of linear maps

This paper proposes and analyzes a stochastic algorithm that provably converges almost surely to the operator norm of the difference between two linear maps—one accessible only via forward evaluation and the other only via adjoint evaluation—by performing a random search on a generalized Rayleigh quotient with optimal step sizes.

Jonas Bresch, Dirk A. Lorenz, Felix Schneppe, Maximilian WinklerTue, 10 Ma🔢 math

Scenario Reduction for Distributionally Robust Optimization

This paper introduces a general scenario reduction method for distributionally robust optimization that projects the original ambiguity set onto a reduced set of scenarios, providing theoretical quality bounds and demonstrating significant computational efficiency with minimal loss in solution accuracy across discrete and continuous distributions.

Kevin-Martin Aigner, Sebastian Denzler, Frauke Liers, Sebastian Pokutta, Kartikey SharmaTue, 10 Ma🔢 math

The State-Dependent Riccati Equation in Nonlinear Optimal Control: Analysis, Error Estimation and Numerical Approximation

This paper analyzes the theoretical foundations, error bounds, and numerical approximations of the State-Dependent Riccati Equation (SDRE) approach for nonlinear optimal control, introducing a residual-minimizing decomposition strategy and demonstrating through numerical experiments that the Newton-Kleinman iterative method offers superior stability and cost-effectiveness compared to the offline-online approach.

Luca SaluzziTue, 10 Ma🔢 math

Alternating Gradient-Type Algorithm for Bilevel Optimization with Inexact Lower-Level Solutions via Moreau Envelope-based Reformulation

This paper proposes the Alternating Gradient-type algorithm with Inexact Lower-level Solutions (AGILS), which leverages a Moreau envelope-based reformulation to efficiently solve convex composite bilevel optimization problems without requiring exact lower-level solutions, while establishing convergence guarantees and demonstrating effectiveness through numerical experiments.

Xiaoning Bai, Shangzhi Zeng, Jin Zhang, Lezhi ZhangTue, 10 Ma🔢 math

Eckstein-Ferris-Pennanen-Robinson duality revisited: paramonotonicity, total Fenchel-Rockafellar duality, and the Chambolle-Pock operator

This paper revisits the Eckstein-Ferris-Pennanen-Robinson duality framework to demonstrate how paramonotonicity ensures the alignment of saddle points with the solution set, characterizes total Fenchel-Rockafellar duality in the subdifferential setting, and derives projection formulas relevant to the Chambolle-Pock algorithm.

Heinz H. Bauschke, Walaa M. Moursi, Shambhavi SinghTue, 10 Ma🔢 math

Minimax Linear Regulator Problems for Positive Systems

This paper derives explicit solutions for a multi-disturbance minimax Linear Regulator problem applied to positive linear time-invariant systems in continuous time, utilizing dynamic programming and a fixed-point method to address both finite and infinite horizons while demonstrating scalability through a large-scale water management network application.

Alba Gurpegui, Mark Jeeninga, Emma Tegling, Anders RantzerTue, 10 Ma🔢 math

A fresh look into variational analysis of $\mathcal C^2$ -partly smooth functions

This paper provides a fresh variational analysis of $\mathcal C^2$ -partly smooth functions by establishing their strict twice epi-differentiability and calculating their second subderivatives, while demonstrating that the converse does not hold and applying these results to the stability of generalized equations and the asymptotic analysis of sample average approximations.

Nguyen T. V. Hang, Ebrahim SarabiTue, 10 Ma🔢 math

New Heuristics for the Operation of an Ambulance Fleet under Uncertainty

This paper proposes and evaluates a set of new heuristics for ambulance selection and reassignment decisions under uncertainty, demonstrating through a rollout approach applied to real-world emergency medical service data that these methods significantly outperform existing strategies in reducing response times while maintaining real-time computational efficiency.

Vincent Guigues, Anton J. Kleywegt, Victor Hugo NascimentoTue, 10 Ma🔢 math

Distributional stability of sparse inverse covariance matrix estimators

This paper establishes the distributional stability of sparse inverse covariance matrix estimators by deriving explicit local Lipschitz bounds on the Kantorovich distance between their distributions under true and contaminated data scenarios, while also extending these results to standard covariance and eigenvalue estimators.

Renjie Chen, Huifu Xu, Henryk ZähleTue, 10 Ma🔢 math

Home Energy Management under Tiered Peak Power Charges

This paper proposes a model predictive control policy for home battery management under tiered peak power charges that, using simple forecasts, achieves electricity costs within 1.7% of the theoretical minimum derived from perfect foresight.

David Pérez-Piñeiro, Sigurd Skogestad, Stephen BoydTue, 10 Ma🔢 math

Erratum and original of Port-Hamiltonian structure of interacting particle systems and its mean-field limit

This paper presents a minimal port-Hamiltonian formulation for interacting particle systems to analyze their stability and mean-field limits, while simultaneously issuing an erratum that corrects a previous claim regarding trajectory compactness by providing a counterexample for repulsive interactions and a revised proof for Hamiltonian gradient convergence.

Jannik Daun, Daniel Jannik Happ, Birgit Jacob, Claudia TotzeckTue, 10 Ma🔢 math

Bilevel Optimization and Heuristic Algorithms for Integrating Latent Demand into the Design of Large-Scale Transit Systems

This paper introduces a bilevel optimization model (TN-DA) for designing large-scale transit networks that integrates latent demand through rider adoption behavior, and proposes five efficient heuristic algorithms that outperform exact methods in computational speed while maintaining high solution quality and key adoption properties in real-world case studies.

Hongzhao Guan, Beste Basciftci, Pascal Van HentenryckTue, 10 Ma🔢 math

Optimal Savings under Transition Uncertainty and Learning Dynamics

This paper establishes the existence and structural properties of optimal consumption and saving policies under transition uncertainty with Bayesian learning, demonstrating how uncertainty about regime persistence shapes precautionary motives and long-run wealth accumulation through a newly identified dynamic mechanism.

Qingyin Ma, Xinxin ZhangTue, 10 Ma🔢 math

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