arXiv subjects

Sara Veneruso

Publications and source records attributed to Sara Veneruso.

3 recordsLinked to original sources

arXiv2026

Exploiting Structure with Anisotropic Consensus-Based Optimization

Anisotropic consensus-based optimization (CBO), a multi-agent metaheuristic derivative-free optimization method, which reliably finds global minima of nonsmooth and nonconvex objective functions while being amenable to rigorous theoretical analysis, automatically detects and exploits additively separable structures of high-dimensional objective functions. This enables the algorithm to mitigate the curse of dimensionality where the objective function decomposes additively into lower-dimensional components. In this paper, we show this property proving that the computational complexity of anisotropic CBO depends exponentially only on the intrinsic dimension $\mathbf{d}$ of the objective function, rather than the ambient dimension $D\gg\mathbf{d}$. Additionally, we demonstrate that the computational complexity depends only on tractability conditions of the lower-dimensional components rather than on the full energy landscape, allowing for a more refined description of the objective function and algorithmic complexity, as the objective function landscape is captured directly at the level of the individual components. Our results highlight the effectiveness of anisotropic CBO for additively separable objective functions provided sufficient alignment between the structure of the anisotropic noise and the separability structure of the objective. This motivates the design of an enhanced algorithm that learns during optimization how to effectively explore the loss landscape by aligning the noise with the structure of the objective function, which we leave for future research. Numerical experiments validate our theoretical results, accentuating the influence of the intrinsic dimensionality $\mathbf{d}$, the level of separability, and the complexity and non-convexity of the objective within the separable components on performance and computational complexity of the anisotropic CBO algorithm.

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arXiv2025

Strong Global Convergence of the Consensus-Based Optimization Algorithm

Consensus-based optimization (CBO) is a multi-agent metaheuristic derivative-free optimization algorithm that has proven to be capable of globally minimizing nonconvex nonsmooth functions across a diverse range of applications while being amenable to theoretical analysis. The method leverages an interplay between exploration of the energy landscape of the objective function through a system of interacting particles subject to stochasticity and exploitation of the particles' positions through the computation of a global consensus about the location of the minimizer based on the Laplace principle. In this paper, we prove strong mean square convergence of the practical numerical time-discrete CBO algorithm to the global minimizer for a rich class of objective functions. For CBO with both isotropic and anisotropic diffusion, our convergence result features conditions on the choice of the hyperparameters as well as explicit rates of convergence in the time discretization step size $Δt$ and the number of particles $N$. By interpreting the time-discrete algorithm at the continuous-time level through a system of stochastic differential equations (SDEs), our proof strategy combines traditional finite-time convergence theory for numerical methods applied to SDEs with careful considerations due to the fact that the CBO coefficients do not satisfy a global Lipschitz condition. To accomodate the latter, we adopt a recently proposed generalization of Sznitman's classical argument, which allows to discard an event of small probability, controllable through fine moment estimates for the particle systems.

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arXiv2025

Micro-Macro Decomposition of Particle Swarm Optimization Methods

Solving non-convex minimization problems using multi-particle metaheuristic derivative-free optimization methods is still an active area of research. Popular methods are Particle Swarm Optimization (PSO) methods, that iteratively update a population of particles according to dynamics inspired by social interactions between individuals. We present a modification to include constrained minimization problems using exact penalization. Additionally, we utilize the hierarchical structure of PSO to introduce a micro-macro decomposition of the algorithm. The probability density of particles is written as a convex combination of microscopic and macroscopic contributions, and both parts are propagated separately. The decomposition is dynamically updated based on heuristic considerations. Numerical examples compare the results obtained using the algorithm in the microscopic scale, in the macroscopic scale, and, using the new micro-macro decomposition.

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