63,467 papers in this slice of arXiv.
Haoyi You, Kaiqing Zhang
In this paper, we formalize a joint communication-control strategy optimization (JCCO) problem in multi-agent linear systems with quadratic costs, under the common-information-based (CIB) framework from decentralized stochastic control. For computational tractability, we focus on such JCCO problems with partially nested (PN) information structures (ISs). In particular, with a baseline communication protocol that leads to a PN IS, we establish a series of conditions under which the partial nestedness is preserved under the (additional) communication strategies to be optimized, while violating them may cause nonlinearity of the optimal strategies in general, with open-loop communication strategies. We then develop a dynamic-programming-based approach to compute the optimal control strategies of JCCO with open-loop communication strategies, which yields a set of closed-form Riccati Equations. As a byproduct of independent interest, such an approach also offers a way to solve decentralized linear-quadratic control with PN ISs and output feedback, under the CIB framework. Finally, we extend such an approach to JCCOs with closed-loop communication strategies, yielding a more tractable dynamic program than an infinite-dimensional CIB-belief-based one.
Omayra Yago Nieto, Alexandre Anahory Simoes, Leonardo Colombo
We study safety-critical control for teams of quadrotor UAVs driven by decentralized navigation functions under learned model uncertainty. These functions generate fully actuated translational reference forces, while quadrotors can only produce thrust along their body-fixed vertical axes. We construct a thrust-attitude implementation of the induced navigation forces and quantify its error with respect to the fully actuated reference dynamics. An aggregated robust HOCBF-QP safety filter minimally modifies the nominal thrusts while guaranteeing pairwise collision avoidance with high probability.
Tiankuo Zhang, Jihye Jung, Paria Nourmohammadi +3
The less-than-truckload (LTL) industry plays a vital role in enhancing the efficiency and sustainability of logistics systems, as LTL shipments offer greater consolidation opportunities than full-truckload shipments. Despite of this flexibility, the average cost of LTL shipments remains considerably higher due to less efficient operations and highly fragmented networks of small and medium-sized carriers. Building on our ongoing effort to develop a distributed and dynamic logistics hub network system grounded in the Physical Internet (PI) principles of modular containers and open resource sharing, this study focuses specifically on inter-hub and in-hub operations, with cooperation among multiple regional hub networks. Therefore, a shipment may traverse multiple cooperating hub networks. With respect to each hub network each shipment enters, it is defined by its expected arrival time at the entry hub and its latest arrival time at the exit hub. Based on the defined shipment information, we design a set of multi-hub operation planning protocols for distributed hub operators. In their operating networks, operators use our smartly designed protocol separately to plan in-hub shipments' assignments to destination-specific trailers and inter-hub trailers' dispatch schedules. With carefully designed interconnections between hub networks, the aggregated hub network system is well-positioned to achieve cooperative outcomes and fulfill shipment requests. We evaluate the effectiveness of the proposed protocol through a simulation-based experiment under multiple scenarios in an operator's multi-hub network. Overall, this research improves the practicality and robustness of PI-based networks and supports greater cooperation among hub networks toward more efficient and sustainable logistics systems.
Adam Y. Shavit
In restricted assignment - makespan minimization where each job has one size and a set of allowed machines - the configuration LP is the tightest studied relaxation, and its integrality gap is open in general. On two-weight graph balancing - each job allowed on at most two machines, sizes from two values - the value is known, both bounds due to Jansen, Land, and Maack (2016): their Table 1 instance attains 3/2, and their Corollary 11 bound of 2 - s/b for sizes s < b meets it at {1,2}. We ask how small such an instance - a witness - can be. We give I*, a six-job witness: the complete graph on four machines, unit jobs on a Hamiltonian cycle, weight-2 jobs on the complementary perfect matching, with integral optimum 3 against relaxation value 2. That is one job fewer than the smallest previously in print, and we prove it minimum and unique at its size. No instance of the class with at most five jobs reaches gap 3/2, on any number of machines; at six jobs, again on any number of machines, I* is the only witness, up to relabeling machines and adding machines no job can use. At seven jobs uniqueness fails: exactly thirteen witnesses, classified - the Jansen-Land-Maack instance among them - and at eight jobs exactly 154. Three machines never suffice, at any size: four are necessary for the gap. Results of this shape are in print for the same relaxation in one-dimensional cutting stock, where the extremal non-round-up instances have been enumerated and classified for small demand; the Discussion sets out the relation. Recognizing witnesses at relaxation value 2 - where all of ours live - is coNP-complete, so no min-max characterization exists unless NP = coNP. Every feasibility decision behind the exhaustive claims was made twice, in floating point and in exact rational arithmetic, with full agreement, and the pipeline must rediscover I* before its negatives are believed.
Yuya Yamakawa, Mamoru Oka
We propose a stabilized sequential quadratic programming (SQP) method for degenerate constrained optimization problems on Riemannian manifolds. The problem considered in this study is a Riemannian nonlinear programming problem (RNLP) with equality and inequality constraints, where classical constraint qualifications may fail. While existing Riemannian SQP methods guarantee global convergence only under constraint qualifications, their convergence behavior is not ensured for degenerate problems. To address this limitation, we extend the stabilized SQP framework from Euclidean spaces to Riemannian manifolds. Without assuming any constraint qualification, we prove that the generated sequence has an accumulation point that is a Karush--Kuhn--Tucker (KKT) point, an approximate KKT (AKKT) point, or a stationary point of an associated feasibility problem. Finally, we conduct numerical experiments to confirm the effectiveness of the proposed method for degenerate problems.
Violaine Piengeon, Joseph J. Winkin
This paper provides a complete analytical study of the positive stabilization, positive state estimation, and observer-based positive stabilization problems for a generic boundary control diffusion system with point observation. In particular, optimal design problems are solved analytically, and an original strategy for observer-based feedback stabilization is proposed. The three problems are also investigated for an arbitrary-order spatial discretization of the nominal PDE system enabling numerical implementation together with rigorous comparisons with the nominal solutions. In both the continuous and discretized settings, explicit and readily implementable solutions are derived.
Han Dong, Jiaming Li, Yongqiang Gong +2
We develop the statistical and algorithmic theory of inverse optimal transport (IOT) under the feature-parameterized cost C_theta(i,j) = -theta^T phi(i,j). The core technical contribution is the Sinkhorn linearization -- the implicit-function sensitivity of the entropic OT plan to the cost -- together with its spectral proxy, a formula that is spectrally exact yet geometrically transparent. The restricted Hessian on the tangent space satisfies the spectral sandwich (pi_min/epsilon) I <= H_T^{-1} <= (pi_max/epsilon) I, yielding the single core bound sigma_min >= (pi_min/(a_max epsilon)) sqrt(lambda_min(Sigma)) that drives the entire theory. On this core we establish four theorems and one observation. T1 (identifiability): theta is globally injective on the quotient of the gauge kernel, with dimension bound F <= (K-1)^2. T2 (sparsistency): the l1-penalized estimator recovers the true support under irrepresentability and score concentration, with exponential failure probability. T3 (well-posedness): the feature-moment map M(theta) = Phi^T x_theta is strongly monotone, and the inverse is Lipschitz with constant L <= epsilon ||Phi^T S_a||_op / (pi_min lambda_min(Sigma)). T4 (convergence): local strong convexity with mu >= pi_min^2 lambda_min(Sigma) / epsilon^2 guarantees monotone gradient descent convergence. O5 (misspecification): the estimator converges to the OT-model projection of the truth; the Holder continuity of the projection map is assessed numerically, yielding setting-dependent empirical exponents alpha_eff in (0,1).
Jinhui Bai, Shuai Lu, Lei Shi
In this paper, we extend stochastic mirror descent (SMD) to infinite-dimensional Banach spaces for solving a class of risk functional minimization problems, where stochastic gradient information is only available through sampling. We first choose the Bregman distance according to the uniform convexity properties of the Banach space. For the non-uniformly convex space LρX1(Ω), we instead construct a Bregman distance induced by the entropy function. Based on a Schauder basis of the Banach space, we introduce a family of finite-dimensional subspaces that adapt to the sample size n. At each SMD iteration, we restrict the subproblem to the corresponding finite-dimensional subspace and project the stochastic gradient onto the associated finite-dimensional dual space, thereby introducing a new regularization strategy. This regularization strategy allows us to explicitly solve the SMD subproblem efficiently and to achieve a bias--variance trade-off. The algorithm requires O(n1+θ) time and O(nθ) memory, where θ>0 can be chosen arbitrarily small when the minimizer has sufficient regularity. By developing a new analytical framework, we prove that the proposed algorithm achieves a convergence rate of O(n−1/p1) up to logarithmic factors, where p1≥2 is determined by the convexity properties of the underlying space. In the misspecified setting where the minimizer satisfies only weaker regularity conditions, we show that the proposed algorithm still converges to the minimum. We further extend the algorithm to inverse problems and validate its effectiveness in numerical experiments.
Jinhyung Bae
Neural combinatorial optimization (NCO) solvers report the best of many sampled solutions per instance, and the sample count is, by convention, identical for every instance. Whether a non-uniform allocation of a fixed total budget would buy anything has not been measured. We measure it, and we audit the measurement itself. First, on in-distribution workloads the allocation headroom is not detectable. Across three pretrained solvers (POMO, AM, SymNCO) on uniform TSP-100, an oracle allocation computed and evaluated on the same stored samples reports a 2.2-2.6% gain with intervals excluding zero; measured out of sample the same gain is indistinguishable from zero (0.457, 0.015, -0.512 percent). Following the customary in-sample procedure, all three solvers would have supported a published 2%-level gain that does not exist. We calibrate this bias against an instance-wise null in which the true gain is zero by construction; over the ranges we test it does not shrink with more samples or more instances. Second, the same correction that removes the phantom gains preserves a real one. Under distribution shift (a workload mixing uniform and clustered instances), a pre-registered confirmatory experiment finds that allocation guided by held-out sample statistics improves best-of-k by 11.5% (AM, primary endpoint; 95% CI [7.4, 19.7]) and 12.0% (SymNCO, replication) at equal evaluation budget, with the signal-acquisition cost not charged; a pre-registered negative control (POMO, an order of magnitude more robust to shift) shows -0.3% [-0.7, 0.24]. The gain exceeds a frozen distribution-label baseline by 4.2 points [1.9, 7.7]. An exploratory policy charging a 20-sample probe against the same budget retains 3.4% (AM) and 4.6% (SymNCO). We give a correction procedure and a reporting checklist, and release all data, code, and the pre-registration record.
Dechen Zhang, Xuan Tang, Xinxiang Yin +3
Machine learning theory studies learning procedures through mathematical setups in which the data model, training protocol, oracle access, loss, metric, and randomness define the phenomenon that a theorem is meant to explain. Solving an open problem therefore requires the problem formulation, theorem target, and proof mechanism to be developed in concert. Researchers formulate hypotheses, test them through preliminary theoretical or empirical analysis, and refine both assumptions and proofs. We investigate whether this process can be organized as an autonomous agentic workflow for ML theory research. We develop VALG, an agentic system that combines multi-level Verification, Adaptive formulation of Learning-theory problems, and Graph-structured proof development. Within each source-relative theorem branch, VALG maintains a fixed mathematical specification, checks the theorem-level composition of a typed proof-dependency graph, and constructs and reviews local proofs in dependency order. When a proof attempt fails, VALG identifies whether the obstruction lies in a derivation, the proof structure, or the theorem formulation and routes the next attempt accordingly. Formulation-level obstructions initiate an explicitly related variant or relaxation, preserving the mathematical relation between the resulting theorem and the source problem. We evaluate VALG on nine subproblems from five COLT 2026 open problems. Two runs produce internally finalized theorem candidates that match the scope of their source briefs; the remaining seven yield restricted-method results, special cases, or conditional theorems. These case studies show how VALG keeps source-scope matches, relaxations, conditional results, and blocked attempts mathematically distinct. VALG is open source at https://github.com/DechenZhang/VALG-ML-Theory-Agent.
Ludovico Ambrosi, Chandra Bortolotto, Sara Cambiaghi +3
Scheduling follow-up Computed Tomography (CT) examinations requires balancing two competing objectives: assigning patients as close as possible to their recommended examination dates while ensuring an equitable distribution of radiologists' workload. Existing approaches optimize scanner utilization or patient waiting times, overlooking reporting activities and the need to balance fairness across multiple stakeholders. This paper proposes a predictive-prescriptive framework for fairness-aware follow-up CT scheduling. Patient-specific Machine Learning (ML) models are first developed to predict both examination and reporting durations. These predictions are then embedded into a multi-objective Mixed-Integer Linear Programming (MILP) model that simultaneously minimizes deviations from patients' preferred examination dates and balances radiologists' reporting workloads through a lexicographic min-max fairness criterion. We derive a dominance reduction property within an ε-constraint framework that substantially reduces the number of optimization problems required to generate the Pareto frontier. Computational experiments based on data from a real-world emergency radiology department show that allowing patients a scheduling flexibility of only one to two days is sufficient to substantially improve workload equity among radiologists while preserving timely access to follow-up examinations. The proposed dominance reduction strategy eliminates most ε-constraint evaluations without affecting the Pareto frontier. Finally, evaluating predictive models through downstream optimization regret demonstrates that XGBoost provides the most effective support for scheduling decisions, outperforming models that achieve lower prediction errors according to conventional predictive metrics.
Janina Schaa, Maria Charitidou, Dimos Dimarogonas +1
We consider distributed distance-based formation control with prescribed transient performance for multi-agent systems modeled by unknown nonlinear dynamics of relative degree greater than one. We introduce a virtual leader whose trajectory is tracked with prescribed transient behavior by a designated subset of agents. The undirected communication graph is either a tree graph or a minimally and infinitesimally rigid graph. In the latter case, we further consider the objective of centroid tracking. In each setting, a distributed and model-free control law is developed, and we establish the satisfaction of the prescribed funnel constraints on the formation and tracking errors, and boundedness of all closed-loop signals. Numerical simulations illustrate the effectiveness of the proposed control laws.
Kaito Ito, Anqi Dong
Distribution steering seeks feedback laws that drive the state law of a dynamical system between prescribed initial and terminal distributions. Optimal transport provides a natural geometric approach, but its implementation generally requires a transport map or coupling in the full state space. Sliced optimal transport avoids this full-dimensional construction through one-dimensional projections. Yet, the resulting projected maps specify only directional displacements and do not by themselves prescribe a realizable feedback law. To this end, we develop a finite-horizon control framework based on sliced optimal transport. At each sampling instant, a projected optimal transport map defines a directional terminal condition, whose minimum-energy realization yields a randomized single-direction controller. Averaging over projection directions gives a deterministic sliced feedback. For the single-integrator dynamics, the averaged feedback makes the sliced Wasserstein distance to the target non-increasing. For Gaussian endpoint laws, it is affine, preserves Gaussianity, and steers the mean and covariance to their prescribed terminal values. We further identify a law-dependent gain that yields linear decay of the sliced Wasserstein distance together with an explicit characterization of the control energy. We also prove that the randomized controller converges to the averaged sliced flow as the sampling period vanishes. Finally, we extend the construction to linear dynamical systems. Reachability-normalized coordinates allow instantaneous realization of the sliced velocity for uniformly fully actuated systems, while local controllability Gramians provide exact finite-step realization for general controllable systems. Numerical examples illustrate the resulting distributional flows.
Víctor Hernández-Santamaría
In this paper, we prove the local null controllability of the Boussinesq system in dimensions two and three with a reduced number of localized controls. More precisely, the controls act on the temperature equation and on only N−2 components of the velocity equation. Our proof is based on a spectral approach in the spirit of the Lebeau--Robbiano method. The main difficulty comes from the coupled structure of the system. The velocity and temperature equations are governed by the Stokes operator and the Dirichlet Laplacian, respectively. Their natural spectral decompositions are different, and there is no common spectral localization naturally adapted to the coupled system. Therefore, the usual componentwise spectral argument cannot be applied directly. We show that the cascade structure of the linearized system makes it possible to overcome this difficulty. The main ingredient is a mixed observability estimate in which only the Stokes component is spectrally localized, while the heat component is treated without any frequency restriction. Combining this estimate with the dissipation of the high Stokes frequencies allows us to carry out a Lebeau--Robbiano iteration for the linearized system. The resulting linear controllability estimate is transferred to the nonlinear Boussinesq system through a time-iteration argument. As an important consequence, the controls have a small-time cost bounded by Cexp(C/T), which recovers the expected parabolic order while preserving the reduced number of controls.
Jonathan M. Bosnich, Xudong Chen
We consider a countably infinite collection of linear, scalar control systems, where each system is represented by the pair (an,bn) with an>0 for n∈N, and all systems are forced by a common scalar control input. We refer to this collection of systems as a discrete linear ensemble system. The ensemble system is feedback stabilizable if there exists a common feedback control input that asymptotically stabilizes every system simultaneously. Unlike finite-dimensional linear systems, an infinite-dimensional linear system is not guaranteed to be stable if its poles lie in the open left half-plane. Stability is guaranteed, however, if (i) its poles are contained in the closed left half-plane and (ii) its infinitesimal generator is diagonalizable. In this paper, we provide necessary and sufficient conditions for the existence of a static, linear feedback control law that ensures the closed-loop ensemble system satisfies (i) and (ii). In particular, we show that the exponential decay of the sequences (∣bn∣)n∈N and (an/∣bn∣)n∈N is necessary and, under an assumption on the desired poles, sufficient.
Lijun Bo, Zhen Liu, Jingfei Wang +1
Global warming, driven by anthropogenic carbon emissions with transboundary pollution characteristics and irreversible damage, poses an existential threat to human society. This paper develops a novel two-level Stackelberg game with mean field interaction of controls and common noise which integrates hierarchical decision-making under a state-reflected emission dynamics. A central regulator (leader) adjusts product prices to guide n heterogeneous competing regions (followers) while enforcing a hard emission cap via a reflection mechanism that models emergency reductions through a local time process. We establish the existence of an approximate Stackelberg equilibrium and perform sensitivity analysis via Monte Carlo simulations.
Floriane Mefo Kue, Patrick Mehlitz, Thorsten Raasch
This paper is devoted to the introduction and analysis of a penalty-type method for the numerical treatment of a class of bilevel optimization problems arising from inverse optimal control. The algorithm is designed to compute stationary points of the associated relaxed value function reformulation. This is achieved by determining a sequence of stationary points associated with a sequence of surrogate problems where the relaxed value function constraint is penalized, where the updates of upper- and lower-level decision variables are decoupled, and where the penalty parameter is enlarged only in those iterations which do not come along with a sufficient improvement of some feasibility measure. The resulting method does not comprise any linesearch, the lower-level problem has to be evaluated just once per iteration, and the penalty parameter does not need to be driven to infinity. Nevertheless, subsequential convergence results are obtained under reasonable assumptions. Numerical experiments, where the relaxation parameter is also driven to zero, visualize effectiveness of the approach.
Daegyun Choi, Donghoon Kim, Henzeh Leeghim
In-space servicing has been receiving great attention to extend the operation of spacecraft with defective components. This requires rendezvous and proximity operations for a chaser to provide service to a target. This work constructs a fuzzy inference system-based controller for the chaser to reach the cooperative target on a circular orbit in the final approach phase while minimizing the energy consumption of the chaser. The offline training process performed by a genetic algorithm deals with multiple initial relative positions of the chaser, and the trained controller is validated using a testing environment with disturbances, which differs from the training scenarios.
Liping Tao, Chee Wei Tan
Laplacian-regularized minimization is fundamental in signal processing and machine learning, but is limited by the dense and ill-conditioned nature of the graph Laplacian pseudoinverse. While the Laplacian itself is sparse, its pseudoinverse is dense and often ill-conditioned, rendering direct computation impractical at scale. Moreover, pseudoinverse learning is more challenging than Laplacian learning. To address this challenge, this paper considers the setting where the graph Laplacian is given and proposes a Difference-of-Convex Regularizer (DCR) graph learning framework that approximates the spectral action of the Laplacian pseudoinverse without direct inversion via regularized Maximum Likelihood Estimation (MLE). By reformulating Laplacian-Regularized Nonnegative Least Squares (LR-NNLS) through a dual representation, DCR decouples pseudoinverse learning from instance-specific inference and enables efficient primal solution reconstruction via a differentiable dual-guided learning scheme. We establish theoretical guarantees on stability and the existence of a unique fixed point for DCR algorithm. Numerical experiments demonstrate improved performance over convex solvers and graph filtering baselines and robust performance across diverse graph topologies.
Dong-Hui Yang, Yuanzhi Zhou
In this paper, we study the observability and controllability of a class of degenerate hyperbolic equations with a control region intersecting the degenerate set. Unlike the existing results that mainly deal with control regions separated from the degeneracy, we consider the case where the control region reaches the degenerate part. To handle the difficulty caused by the degeneracy, we introduce a shape-design-based approximation method based on shape design by approximating the degenerate equation with a family of uniformly hyperbolic equations. The proof relies on the spectral approximation of the associated operators, precise estimates for weak solutions, and the multiplier method. We first establish observability inequalities for the approximating equations with constants independent of the approximation parameter. Then, by passing to the degenerate limit, we obtain the observability inequality for the original degenerate equation.