Search arXivSearch

arXiv · 2005.05609

Legendre's necessary condition for fractional Bolza functionals with mixed initial/final constraints

Abstract

The present work was primarily motivated by our findings in the literature of some flaws within the proof of the second-order Legendre necessary optimality condition for fractional calculus of variations problems. Therefore we were eager to elaborate a correct proof and it turns out that this goal is highly nontrivial, especially when considering final constraints. This paper is the result of our reflections on this subject. Precisely we consider here a constrained minimization problem of a general Bolza functional that depends on a Caputo fractional derivative of order 0 < $α$ $\le$ 1 and on a Riemann-Liouville fractional integral of order $β$ > 0, the constraint set describing general mixed initial/final constraints. The main contribution of our work is to derive corresponding first-and second-order necessary optimality conditions, namely the Euler-Lagrange equation, the transversality conditions and, of course, the Legendre condition. A detailed discussion is provided on the obstructions encountered with the classical strategy, while the new proof that we propose here is based on the Ekeland variational principle. Furthermore we underline that some subsidiary contributions are provided all along the paper. In particular we prove an independent and intrinsic result of fractional calculus stating that it does not exist a nontrivial function which is, together with its Caputo fractional derivative of order 0 < $α$ < 1, compactly supported. Moreover we also discuss some evidences claiming that Riemann-Liouville fractional integrals should be considered in the formulation of fractional calculus of variations problems in order to preserve the existence of solutions.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Loïc Bourdin, Rui A. C. Ferreira. 2021-07-08. Legendre's necessary condition for fractional Bolza functionals with mixed initial/final constraints. https://arxiv.org/abs/2005.05609

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

KEEP EXPLORING

Related papers

The Operational Impact of Registry size, Cycle length, and Blood Type Distribution in Multi-Registry Kidney Exchange Programs

Kidney exchange programs address donor recipient incompatibility by exchanging donors between incompatible pairs, but single center KEPs often suffer from limited donor pools, which reduce matching efficiency. Multi registry kidney exchange programs offer a promising solution but face challenges, including heterogeneous constraints across registries, cycle-length bounds, and data-sharing limitations. This study uses simulation to compare mKEP allocation against individual registry operation, contrasting unconstrained pooling with two safeguarded mechanisms: a cumulative individual-rationality guarantee and a Shapley value based fair-share mechanism. We examine how registry size, blood-type distribution, cycle-length bounds, and dropout probability affect the size and distribution of achievable gain. Our central finding is that the benefit of joining an mKEP is systematically uneven: under blood group composition asymmetry, the easier-to-match registry gains less than its partner even under both safeguarded mechanisms, and a larger registry gains less than a smaller one when pooled. A registry combining a larger arrival rate with an easier-to-match pool may see its gain under either safeguarded mechanism fall too small to be practically significant, while registries with a higher dropout rate see a larger benefit from pooling. This unevenness concentrates in O-type recipients: an easier-to-match registry sees fewer O-type transplants within itself under pooling, though O-type transplant rates rise system-wide. Tighter cycle-length bounds increase, rather than diminish, the relative transplant-volume benefit of pooling, while match quality is only modestly affected by any factor examined. These results highlight the importance of pairing multi-registry collaboration with a carefully designed, equitable benefit-sharing mechanism to keep participation attractive for all registries involved.

math.OC

A polynomial approximation scheme for nonlinear model reduction by moment matching

We propose a procedure for the numerical approximation of invariance equations arising in the moment matching technique associated with reduced-order modeling of high-dimensional dynamical systems. The Galerkin residual method is employed to find an approximate solution to the invariance equation using a Newton iteration on the coefficients of a monomial basis expansion of the solution. These solutions to the invariance equations can then be used to construct reduced-order models. We assess the ability of the method to solve the invariance PDE system as well as to achieve moment matching and recover the steady-state behaviour of nonlinear systems with state dimension of order 1000 driven by linear and nonlinear signal generators.

math.OC

Computationally Efficient Density-Driven Optimal Control via Analytical KKT Reduction and Contractive MPC

Efficient coordination for collective spatial distribution is a fundamental challenge in multi-agent systems. Prior research on Density-Driven Optimal Control (D2OC) established a framework to match agent trajectories to a desired spatial distribution. However, implementing this as a predictive controller requires solving a large-scale Karush-Kuhn-Tucker (KKT) system, whose computational complexity grows cubically with the prediction horizon. To resolve this, we propose an analytical structural reduction that transforms the T-horizon KKT system into a condensed quadratic program (QP). This formulation achieves O(T) linear scalability, significantly reducing the online computational burden compared to conventional O(T^3) approaches. Furthermore, to ensure rigorous convergence in dynamic environments, we incorporate a contractive Lyapunov constraint and prove the Input-to-State Stability (ISS) of the closed-loop system against reference propagation drift. Numerical simulations verify that the proposed method facilitates rapid density coverage with substantial computational speed-up, enabling long-horizon predictive control for large-scale multi-agent swarms.

math.OC