Characterizations, Dynamical Systems and Gradient Methods for Strongly Quasiconvex Functions

Felipe Lara (), Raúl T. Marcavillaca () and Phan Tu Vuong ()
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Felipe Lara: Universidad de Tarapacá
Raúl T. Marcavillaca: Universidad de Chile
Phan Tu Vuong: University of Southampton

Journal of Optimization Theory and Applications, 2025, vol. 206, issue 3, No 4, 25 pages

Abstract: Abstract We study differentiable strongly quasiconvex functions for providing new properties for algorithmic and monotonicity purposes. Furthermore, we provide insights into the decreasing behaviour of strongly quasiconvex functions, applying this for establishing exponential convergence for first- and second-order gradient systems without relying on the usual Lipschitz continuity assumption on the gradient of the function. The explicit discretization of the first-order dynamical system leads to the gradient descent method while discretization of the second-order dynamical system with viscous damping recovers the heavy ball method. We establish the linear convergence of both methods under suitable conditions on the parameters as well as numerical experiments for supporting our theoretical findings.

Keywords: Nonconvex optimization; Quasiconvex function; Dynamical systems; Gradient descent; Heavy ball method; Linear convergence (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10957-025-02728-y

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