强化单侧Lipschitz微分包涵的Filippov近似定理

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED Computational Optimization and Applications Pub Date : 2023-09-11 DOI:10.1007/s10589-023-00517-9
Robert Baier, Elza Farkhi
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引用次数: 1

摘要

摘要:我们考虑具有增强单侧Lipschitz (SOSL)右手边的微分内含物。SOSL多值映射类比Lipschitz映射类更宽,是单侧Lipschitz映射类的一个子类。我们证明了这类微分包体的解的Filippov近似定理,其右边既有速度集(外摄动),也有状态集(内摄动)。得到的近似解和精确解之间距离的估计将已知的Lipschitz映射的Filippov估计扩展到SOSL映射,并将关于单侧Lipschitz (OSL)右手边已知的内部扰动的近似阶数从$$\frac{1}{2}$$ 1 2提高到1。
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A Filippov approximation theorem for strengthened one-sided Lipschitz differential inclusions
Abstract We consider differential inclusions with strengthened one-sided Lipschitz (SOSL) right-hand sides. The class of SOSL multivalued maps is wider than the class of Lipschitz ones and a subclass of the class of one-sided Lipschitz maps. We prove a Filippov approximation theorem for the solutions of such differential inclusions with perturbations in the right-hand side, both of the set of the velocities (outer perturbations) and of the state (inner perturbations). The obtained estimate of the distance between the approximate and exact solution extends the known Filippov estimate for Lipschitz maps to SOSL ones and improves the order of approximation with respect to the inner perturbation known for one-sided Lipschitz (OSL) right-hand sides from $$\frac{1}{2}$$ 1 2 to 1.
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来源期刊
CiteScore
3.70
自引率
9.10%
发文量
91
审稿时长
10 months
期刊介绍: Computational Optimization and Applications is a peer reviewed journal that is committed to timely publication of research and tutorial papers on the analysis and development of computational algorithms and modeling technology for optimization. Algorithms either for general classes of optimization problems or for more specific applied problems are of interest. Stochastic algorithms as well as deterministic algorithms will be considered. Papers that can provide both theoretical analysis, along with carefully designed computational experiments, are particularly welcome. Topics of interest include, but are not limited to the following: Large Scale Optimization, Unconstrained Optimization, Linear Programming, Quadratic Programming Complementarity Problems, and Variational Inequalities, Constrained Optimization, Nondifferentiable Optimization, Integer Programming, Combinatorial Optimization, Stochastic Optimization, Multiobjective Optimization, Network Optimization, Complexity Theory, Approximations and Error Analysis, Parametric Programming and Sensitivity Analysis, Parallel Computing, Distributed Computing, and Vector Processing, Software, Benchmarks, Numerical Experimentation and Comparisons, Modelling Languages and Systems for Optimization, Automatic Differentiation, Applications in Engineering, Finance, Optimal Control, Optimal Design, Operations Research, Transportation, Economics, Communications, Manufacturing, and Management Science.
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