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Related Articles from SNS
$\mathcal{H}_2$-optimal model reduction of linear quadratic-output systems by multivariate rational interpolation
Announce Type: replace Abstract: This paper addresses the $\mathcal{H}_2$-optimal approximation of linear dynamical systems with quadratic-output functions, also known as linear quadratic-output systems. Our major contributions are threefold. First, we derive interpolatory first-order optimality conditions for the linear quadratic-output $\mathcal{H}_2$ minimization problem.
$H_2$ optimal model reduction of linear systems with multiple quadratic outputs
Announce Type: replace Abstract: In this work, we consider the $H_2$ optimal model reduction of dynamical systems that are linear in the state equation and up to quadratic nonlinearity in the output equation. As our primary theoretical contributions, we derive gradients of the squared $H_2$ system error with respect to the reduced model quantities and, from the stationary points of these gradients, introduce Gramian-based first-order necessary conditions for the $H_2$ optimal approximation...
Late-Time Cosmology and Structure Formation in Quadratic $f(Q)$ Gravity
arXiv:2606.02660v1 Announce Type: new Abstract: We investigate the cosmological evolution associated with the quadratic symmetric teleparallel gravity framework, \( f(Q)=Q+\alpha Q^{2}+\beta \) where the relation \(Q\propto H^{2}\) generates an additional \(H^{4}\) contribution to the Friedmann equation. Using the exact algebraic solution for $H(z)$, we reconstruct the effective dark-energy sector and compare the background evolution with $\Lambda$CDM using Type Ia supernovae, BAO, and...
ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs
arXiv:2604.23053v2 Announce Type: replace Abstract: Mixed Binary Quadratic Programs (MBQPs) are an important and complex set of problems in combinatorial optimization. As solving large-scale combinatorial optimization problems is challenging, primal heuristics have been developed to quickly identify high-quality solutions within a short amount of time. Recently, a growing body of research has also used machine learning to accelerate solution methods for challenging combinatorial optimization...
Comparing sliding-mode, bang-bang and linear-quadratic-Gaussian for steering an atomic clock
arXiv:2605.20156v2 Announce Type: replace Abstract: Accurate timekeeping relies on feedback that continually steers a local clock toward a higher-grade reference. We evaluate first-order sliding-mode control (SMC) for steering an atomic clock and benchmark it against two standards: linear-quadratic-Gaussian (LQG) control and the bang-bang (BB). All three are tested in a common numerical framework using the standard two-state clock model driven by white and random-walk-frequency noise.
APX-Hardness of Computing Lipschitz Constants for Multi-Parametric Quadratic Programs
Announce Type: new Abstract: Computing the Lipschitz constant of the solution map of a multi-parametric quadratic program is important for the analysis of optimization-based control. This problem is governed by three factors: the parameter dimension, the number of decision variables, and the number of constraints.
Data-Driven Min-Max MPC with Integral Quadratic Constraints
Announce Type: new Abstract: Data-driven control of nonlinear systems with rigorous guarantees is a challenging control problem. Integral quadratic constraints (IQCs) provide a powerful framework for modeling nonlinearities. This paper presents a data-driven min-max model predictive control (MPC) synthesis method for unknown systems subject to (nonlinear) uncertainties using the IQC framework.
Near-Optimal Mixed Strategy for Zero-Sum Linear-Quadratic Differential Games
Announce Type: cross Abstract: Deriving analytic solutions for optimal mixed strategies in zero-sum linear-quadratic differential games (ZSLQDGs) remains an open problem. In this paper, we analytically synthesize near-optimal mixed strategies for ZSLQDGs and establish rigorous performance certifications. Specifically, we construct a surrogate pure-strategy stochastic differential game (SDG) by matching the first two moments of the mixed strategies.
Solution Sets for Inverse Infinite-Horizon Linear-Quadratic Descriptor Differential Games
arXiv:2604.27460v4 Announce Type: replace-cross Abstract: In this letter, we study a model-based inverse problem for infinite-horizon linear-quadratic differential games with descriptor dynamics. Given an observed feedback strategy profile, we seek to identify all cost functions that rationalize it as a feedback Nash equilibrium; this collection is referred to as the solution set. We characterize the solution set, show that it is rectangular and convex, and provide an algorithm for computing...
Variance-Reduced Model Predictive Path Integral via Quadratic Model Approximation
arXiv:2602.03639v2 Announce Type: replace Abstract: Sampling-based controllers, such as Model Predictive Path Integral (MPPI) methods, offer substantial flexibility but often suffer from high variance and low sample efficiency. To address these challenges, we introduce a hybrid variance-reduced MPPI framework that integrates a prior model into the sampling process. Our key insight is to decompose the objective function into a known approximate model and a residual term.