23 citations · 57 across the 5 of their papers we have counts for
6 papers
A practical DIRECT-type algorithm for medium-scale black-box global optimization
Linas Stripinis, Remigijus Paulavičius
The DIRECT algorithm is a deterministic global optimization method known for its versatility and balanced exploration-exploitation strategy. However, DIRECT-type algorithms are pri…
An extensive numerical benchmark study of deterministic vs. stochastic derivative-free global optimization algorithms
Linas Stripinis, Remigijus Paulavičius
Research in derivative-free global optimization is under active development, and many solution techniques are available today. Therefore, the experimental comparison of previous an…
Challenges and opportunities in applying Neural Temporal Point Processes to large scale industry data
Dominykas Šeputis, Jevgenij Gamper, Remigijus Paulavičius
In this work, we identify open research opportunities in applying Neural Temporal Point Process (NTPP) models to industry scale customer behavior data by carefully reproducing NTPP…
Lipschitz-inspired HALRECT Algorithm for Derivative-free Global Optimization
Linas Stripinis, Remigijus Paulavičius
This article considers a box-constrained global optimization problem for Lipschitz-continuous functions with an unknown Lipschitz constant. Motivated by the famous DIRECT (DIviding…
An empirical study of various candidate selection and partitioning techniques in the DIRECT framework
Linas Stripinis, Remigijus Paulavičius
Over the last three decades, many attempts have been made to improve the DIRECT (DIviding RECTangles) algorithm's efficiency. Various novel ideas and extensions have been suggested…
DIRECTGO: A new DIRECT-type MATLAB toolbox for derivative-free global optimization
Linas Stripinis, Remigijus Paulavičius
In this work, we introduce DIRECTGO, a new MATLAB toolbox for derivative-free global optimization. DIRECTGO collects various deterministic derivative-free DIRECT-type algorithms fo…