Linas Stripinis

• PhD
• Senior Researcher at Vilnius University

Like other disciplines, machine learning is currently facing a reproducibility crisis that hinders the advancement of scientific research. Researchers face difficulties reproducing key results due to the lack of critical details, including the disconnection between publications and associated models, data, parameter settings, and experimental resul...

Derivative-free DIRECT-type global optimization algorithms are increasingly favoured for their simplicity and effectiveness in addressing real-world optimization challenges. This review examines their practical applications through a systematic analysis of scientific journals and computational studies. In particular, significant challenges in repro...

The consensus protocol plays a vital role in the performance and security of a specific Distributed Ledger Technology (DLT) solution. Currently, the traditional classification of consensus algorithms relies on subjective criteria, such as protocol families (Proof of Work, Proof of Stake, etc.) or other protocol features. However, such classificatio...

This paper addresses the challenge of selecting the most suitable optimization algorithm by presenting a comprehensive computational comparison between stochastic and deterministic methods. The complexity of algorithm selection arises from the absence of a universal algorithm and the abundance of available options. Manual selection without comprehe...

Artificial Intelligence, particularly in Machine Learning and related research areas such as Operational Research, currently faces a reproducibility crisis. Researchers encounter difficulties reproducing key results due to lacking critical details, including the disconnection between publications and the associated codes, data, and parameter settin...

This chapter serves as an introduction to the global optimization problems explored within this book, offering a succinct overview of existing derivative-free techniques for global optimization. Specifically, we will position the DIRECT algorithm within other derivative-free global solution techniques, while also exploring its popularity and the di...

The deterministic derivative-free DIRECT-type algorithms have gained significant recognition in the optimization community due to their simplicity and efficiency. This chapter provides a comprehensive review of the advancements made in the development of novel approaches and extensions of the DIRECT algorithm. Although the original DIRECT algorithm...

This chapter discusses the software development aspects of DIRECT-type algorithms. It explores the implementation of both sequential and parallel versions of these algorithms. Various software tools, including the DIRECTGO toolbox and the GENDIRECT algorithmic framework, are discussed in detail. Furthermore, this chapter provides an overview of the...

Over the past three decades, numerous articles have been published discussing the renowned DIRECT algorithm (DIvididing RECTangles). These articles present innovative ideas to enhance its performance and adapt it to various types of optimization problems. A comprehensive collection of deterministic, derivative-free algorithmic implementations based...

This paper introduces an innovative extension of the DIRECT algorithm specifically designed to solve global optimization problems that involve Lipschitz continuous functions subject to linear constraints. Our approach builds upon recent advancements in DIRECT-type algorithms, incorporating novel techniques for partitioning and selecting potential o...

This article considers a box-constrained global optimization problem for Lipschitz-continuous functions with an unknown Lipschitz constant. Motivated by the famous DIRECT (DIviding RECTangles), a new HALRECT (HALving RECTangles) algorithm is introduced. A new deterministic approach combines halving (bisection) with a new multi-point sampling scheme...

After providing an in-depth introduction to derivative-free global optimization with various constraints, this book presents new original results from well-known experts on the subject. A primary focus of this book is the well-known class of deterministic DIRECT (DIviding RECTangle)-type algorithms. This book describes a new set of algorithms deriv...

This article considers a box-constrained global optimization problem for Lipschitz continuous functions with an unknown Lipschitz constant. The well-known derivative-free global search algorithm DIRECT (DIvide RECTangle) is a promising approach for such problems. Several studies have shown that recent two-step (global and local) Pareto selection-ba...

Research in derivative-free global optimization is under active development, and many solution techniques are available today. Therefore, the experimental comparison of previous and emerging algorithms must be kept up to date. This paper considers the solution to the bound-constrained, possibly black-box global optimization problem. It compares 64...

A popular presentation of our recent paper: DOI: 10.1145/3559755

In this work, we introduce DIRECTGO , a new MATLAB toolbox for derivative-free global optimization. DIRECTGO collects various deterministic derivative-free DIRECT -type algorithms for box-constrained, generally-constrained, and problems with hidden constraints. Each sequential algorithm is implemented in two ways: using static and dynamic data stru...

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. The main two steps of DIRECT-type algorithms are selecting and partitioning potentially optimal rectangles. However, the most efficient combination of these two steps i...

This article considers a box-constrained global optimization problem for Lipschitz-continuous functions with an unknown Lipschitz constant. Motivated by the famous DIRECT (DIviding RECTangles), a new HALRECT (HALving RECTangles) algorithm is introduced. A new deterministic approach combines halving (bisection) with a new multi-point sampling scheme...

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. The main two steps of DIRECT-type algorithms are the selection and partitioning of potentially optimal candidates. However, the most efficient combination of these two...

In this paper, we consider the solution of global optimization problems involving hidden constraints. We present a novel deterministic derivative-free global optimization algorithm based on our recently introduced DIRECT-GL (Stripinis et al. in Optim Lett. 12(7):1699–1712, 2018). The new algorithm (DIRECT-GLh) incorporates two additional techniques...

In this work, we introduce DGO, a new MATLAB toolbox for derivative-free global optimization. DGO collects various deterministic derivative-free DIRECT-type algorithms for box-constrained, generally-constrained, and problems with hidden constraints. Each sequential algorithm is implemented in two different ways: using static and dynamic data struct...

In this paper, two different acceleration techniques for a deterministic DIRECT (DIviding RECTangles)-type global optimization algorithm, DIRECT-GLce, are considered. We adopt dynamic data structures for better memory usage in MATLAB implementation. We also study shared and distributed parallel implementations of the original DIRECT-GLce algorithm,...

Applied optimization problems often include constraints. Although the well-known derivative-free global-search DIRECT algorithm performs well solving box-constrained global optimization problems, it does not naturally address constraints. In this article, we develop a new algorithm DIRECT-GLce for general constrained global optimization problems in...

We consider a box-constrained global optimization problem with a Lipschitz-continuous objective function and an unknown Lipschitz constant. The well known derivative-free global-search DIRECT (DIvide a hyper-RECTangle) algorithm performs well solving such problems. However, the efficiency of the DIRECT algorithm deteriorates on problems with many l...