Optimization and Control Papers of the Week

The diagonal self-observations measure uncertainty, while source-centered cross-observations recover the two KL objectives. We establish an exact identity equating the Fisher inverse-metric coupling of their logit gradients with the sum of these contrasts, yielding a common local descent direction for distinct positive distributions.

Can learned latent dynamics models support the design of feedback controllers that provably stabilize the original system? For linear time-invariant systems, we show that learning linear autoencoders and latent dynamics with multi-step prediction-reconstruction losses $\unicode{x2014}$ which we term "backward losses" as they encourage latent trajectories to reconstruct full state trajectories $\unicode{x2014}$ provably recovers the structure needed for stabilization.

They inspect solver logs, identify performance bottlenecks, and adjust the parameters that address them. We propose Log-Driven Semantic Tuning (LDST), which uses a large language model (LLM) to automate this process.

However, existing microgrid-based energy supply architectures in high-altitude construction parks face low energy utilization efficiency, high carbon emissions, and inadequate accounting for market-related costs. To address these issues, this study proposes a low-carbon optimal dispatch method incorporating truck–grid interaction for a high-altitude construction-park integrated energy system (HACP-IES).

This paper proposes a novel mixed-order inertial primal-dual dynamical system with general time-scaling and Tikhonov regularization coefficients, together with implicit Hessian damping, for solving convex optimization problems with linear equality constraints in Hilbert spaces.

We study approximate Nash equilibria in nonatomic congestion games, with the goal of understanding how much the social cost can change when the equilibrium condition is relaxed.

We prove that the problem remains $CLS$-complete even when the coefficients are encoded in unary.

Landing methods allow temporarily infeasible iterates to explore a neighborhood of the manifold while progressively restoring orthogonality without per-step retractions. To improve optimization efficiency while retaining this flexibility, we introduce ProLand (Product-space Landing), a retraction-free optimizer that combines coordinated factor updates with Muon-style spectral directions.

We show that the Frank-Wolfe method is an approximation of Broximal Gradient Descent (BroxGD), and hence of the ball-proximal method.

In this paper, we propose a policy iteration framework that refines receding-horizon control (RHC) and model predictive control (MPC) policies online by iteratively updating the terminal cost.

We consider the optimal control problem of an elliptic PDE with random diffusion under pointwise box constraints on the control.

We propose a worst-case CPT-based shortfall risk measure where the capital requirement is based on the worst-case value function and distortion functions from an ambiguity set constructed with user-specific information (e.g., preference between risky and certain lotteries) and well-known general information such as the S-shape of the value functions.

As a first step toward implementable variants, we study the method when these subproblems are solved only approximately. We introduce and analyze Inexact BPM, which replaces the exact broximal point by an approximation and makes no assumption on how this approximation is obtained.

In this work, we investigate specific structural conditions on the cost matrix under which the constrained version-quadratic binary optimization with a fixed cardinality-remains tractable.

We propose a parallel CPU-based heuristic that combines impact-based variable selection, tabu search, guided restarts, and a shared elite pool.

This paper investigates discrete-time stochastic recursive games driven by fractional noises, in which the cost functional is defined through a backward stochastic difference equation.

Problem definition: We study dynamic inventory control for battery-swapping stations that combine central replenishment with automatic local recovery.

We consider a robust version of the appointment scheduling problem, introduced by Mittal et al.\ (2014), with the goal of finding simple and easy-to-use algorithms.

A single framework based on the grid is presented that integrates predictive intelligence, optimization, adaptive control, cyber-physical systems, explainability, cyber-security, and sustainability in a closed-loop system.