Gradient Descent Methods for Regularized Optimization

Autor: Nikolovski, Filip, Stojkovska, Irena, Saneva, Katerina Hadzi-Velkova, Hadzi-Velkov, Zoran

Regularization is a widely recognized technique in mathematical optimization. It can be used to smooth out objective functions, refine the feasible solution set, or prevent overfitting in machine learning models. Due to its simplicity and robustness,

Externí odkaz: http://arxiv.org/abs/2412.20115

SCALE INVARIANT STOCHASTIC GRADIENT METHOD WITH MOMENTUM.

Autor: NIKOLOVSKI, FILIP, STOJKOVSKA, IRENA

Publikováno v: Matematichki Bilten. Bulletin Mathématique de la Société des Mathématiciens de la République de Macédoine; 2023, Vol. 47 Issue 2, p147-164, 18p

DESCENT DIRECTION STOCHASTIC APPROXIMATION ALGORITHM WITH ADAPTIVE STEP SIZES.

Autor: Lužanin, Zorana¹ zorana@dmi.uns.ac.rs, Stojkovska, Irena² irenatra@pmf.ukim.mk, Kresoja, Milena¹ milena.kresoja@dmi.uns.ac.rs

Publikováno v: Journal of Computational Mathematics. 2019, Vol. 37 Issue 1, p76-94. 19p.

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On the optimality of the optimal policies for the deterministic EPQ with partial backordering

Autor: Stojkovska, Irena

Publikováno v: In Omega October 2013 41(5):919-923