Search arXiv⌕ Search

arXiv · 2610.09750

Necessary Conditions for Free End-Time Sweeping Process Problems

Abstract

This paper derives necessary optimality conditions for free end-time optimal control problems governed by sweeping process dynamics. In contrast to classical optimal control problems, the presence of the normal cone term in the state equation introduces discontinuities with respect to the state variable and prevents the standard Lipschitz regularity assumptions typically used in optimal control theory. As a result, both the analysis of trajectories and the formulation of optimality conditions require refined tools. We address problems with general endpoint constraints and time-dependent data, considering both the case in which the dynamics are Lipschitz continuous in time and the more challenging setting of merely measurable time dependence. Special attention is devoted to the additional transversality conditions associated with the free end-time variable. In the Lipschitz case, these conditions are expressed in terms of a function of bounded variation that coincides almost everywhere with the maximized Hamiltonian. In the measurable case, we show that a suitable interpretation can be obtained via the notions of sub- and super-essential values of the maximized Hamiltonian, recently introduced in the literature, leading to strengthened transversality conditions. These conditions also involve an additional term reflecting the interaction between the costate arc and the normal cone to the moving set at the optimal endpoints. Our approach combines suitable perturbation techniques with a penalization method tailored to sweeping processes. The moving set is modelled as a finite intersection of inequality-defined sets satisfying suitable constraint qualifications, including a positive linear independence condition and a diagonal dominance property of the associated Gramian matrix. We also introduce a local version of the constraint qualification condition. Our results apply to a broad class of problems, including those involving unbounded moving sets such as polyhedra. The resulting optimality system extends and sharpens recent results for classical free end-time problems to the non-Lipschitz, discontinuous framework of sweeping processes. The relevance of the transversality conditions obtained in this article is illustrated through two simple examples.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Piernicola Bettiol, Jérémy Rouot, Paulin Bruneau. 2026-10-07. Necessary Conditions for Free End-Time Sweeping Process Problems. https://arxiv.org/abs/2610.09750

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Perturbed Iterate SGD for Lipschitz Continuous Loss Functions with Numerical Error and Adaptive Step Sizes

Motivated by neural network training in finite-precision arithmetic environments, this work studies the convergence of perturbed iterate SGD using adaptive step sizes in an environment with numerical error. Considering a general stochastic Lipschitz continuous loss function, an asymptotic convergence result to a Clarke stationary point is proven as well as the non-asymptotic convergence to an approximate stationary point in expectation. It is assumed that only an approximation of the loss function's stochastic gradient can be computed, in addition to error in computing the SGD step itself.

math.OC↗

Sharp bounds in perturbed smooth optimization

This paper studies the problem of perturbed convex and smooth optimization. The main results describe how the solution and the value of the problem change if the objective function is perturbed. Examples include linear, quadratic, and smooth additive perturbations. Such problems naturally arise in statistics and machine learning, stochastic optimization, stability and robustness analysis, inverse problems, optimal control, etc. The results provide accurate expansions for the difference between the solution of the original problem and its perturbed counterpart with an explicit error term.

math.OC↗

Controllability Allocation Scores for Targeted Network Intervention

We introduce the controllability allocation score (CAS), a framework for determining how intervention intensity should be distributed among prescribed candidate input directions with respect to designated target variables, together with the target controllability score (TCS) as its nodewise specialization. We establish existence of the CASs and develop a general uniqueness theory based on restricted injectivity of the allocation-to-Gramian map, including generic uniqueness with respect to the time horizon. We show that restricting attention to target variables can fundamentally alter the optimal intervention allocation compared with the standard full-state setting. To enable scalability, we develop a general surrogate-Gramian framework and derive objective-performance guarantees from relative Gramian errors without requiring uniqueness or closeness of the CASs. For the TCS specialization, we further construct a target-only reduced virtual system and derive explicit bounds showing how the approximation error depends on the coupling between target and non-target nodes and on the dynamical growth rate. Experiments on human brain networks show that the reduced formulation accurately approximates the TCS at short horizons, whereas the two controllability criteria exhibit markedly different approximation accuracy at long horizons.

math.OC↗