Lyapunov Stability of Stochastic Vector Optimization: Theory and Numerical Implementation

• URL: http://arxiv.org/abs/2603.04095v1
• Date: Wed, 04 Mar 2026 14:04:24 GMT
• Title: Lyapunov Stability of Stochastic Vector Optimization: Theory and Numerical Implementation
• Authors: Thiago Santos, Sebastiao Xavier,
• Abstract summary: We use a drift--diffusion model for unconstrained vector optimization in which the drift is induced by a common descent direction.<n>We implement the resulting algorithm as an open-source Python framework for multi-objective optimization.<n> Empirical results on DTLZ2 with objective counts from three to fifteen indicate a consistent trade-off.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The use of stochastic differential equations in multi-objective optimization has been limited, in practice, by two persistent gaps: incomplete stability analyses and the absence of accessible implementations. We revisit a drift--diffusion model for unconstrained vector optimization in which the drift is induced by a common descent direction and the diffusion term preserves exploratory behavior. The main theoretical contribution is a self-contained Lyapunov analysis establishing global existence, pathwise uniqueness, and non-explosion under a dissipativity condition, together with positive recurrence under an additional coercivity assumption. We also derive an Euler--Maruyama discretization and implement the resulting iteration as a \emph{pymoo}-compatible algorithm -- \emph{pymoo} being an open-source Python framework for multi-objective optimization -- with an interactive \emph{PymooLab} front-end for reproducible experiments. Empirical results on DTLZ2 with objective counts from three to fifteen indicate a consistent trade-off: compared with established evolutionary baselines, the method is less competitive in low-dimensional regimes but remains a viable option under restricted evaluation budgets in higher-dimensional settings. Taken together, these observations suggest that stochastic drift--diffusion search occupies a mathematically tractable niche alongside population-based heuristics -- not as a replacement, but as an alternative whose favorable properties are amenable to rigorous analysis.

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