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In recent years, there has been a growing interest in the potential of quantum computing for enhancing the security and efficiency of blockchain technology. While quantum blockchain protocols offer improved security over their classical counterparts, implementing such protocols on present-day quantum computers poses difficulties due to the limited number of qubits and quantum gates and the significant effects of noise. In this paper, we propose a set of improvements for implementing a quantum blockchain protocol based on hypergraphs that aim to reduce the required resources and operations and increase noise tolerance. Specifically, we focus on enhancing the state-of-the-art quantum circuits that underpin the quantum blockchain by optimizing the so-called T-count and T-depth, which represent the number of quantum gates and the circuit depth, respectively. Our proposed implementations also leverage proven error detection and correction codes to improve noise tolerance. To evaluate the effectiveness of our proposed improvements, we tested them on real quantum devices. Our results demonstrate a significant reduction in the T-count and T-depth. Overall, our proposed improvements provide a promising direction for the practical implementation of quantum blockchain protocols on current quantum computers and lay a foundation for further research in this area. [ABSTRACT FROM AUTHOR]

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 optimal hyper-rectangles. A key contribution lies in applying a new mapping technique to eliminate the infeasible region efficiently. This allows calculations to be performed only within the feasible region defined by linear constraints. We perform extensive tests using a diverse set of benchmark problems to evaluate the effectiveness and performance of the proposed algorithm compared to existing DIRECT solvers. Statistical analyses using Friedman and Wilcoxon tests demonstrate the superiority of a new algorithm in solving such problems. [ABSTRACT FROM AUTHOR]

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 structures for more efficient information storage and organization. Furthermore, parallel schemes are applied to some promising algorithms within DIRECTGO. The toolbox is equipped with a graphical user interface (GUI), ensuring the user-friendly use of all functionalities available in DIRECTGO. Available features are demonstrated in detailed computational studies using a comprehensive DIRECTGOLib v1.0 library of global optimization test problems. Additionally, 11 classical engineering design problems illustrate the potential of DIRECTGO to solve challenging real-world problems. Finally, the appendix gives examples of accompanying MATLAB programs and provides a synopsis of its use on the test problems with box and general constraints. [ABSTRACT FROM AUTHOR]

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-based algorithms are very efficient among all DIRECT-type approaches. However, despite its encouraging performance, it was also observed that the candidate selection procedure has two possible shortcomings. First, there is no limit on how small the size of selected candidates can be. Secondly, a balancing strategy between global and local candidate selection is missing. Therefore, it may waste function evaluations by over-exploring the current local minimum and delaying finding the global one. This paper reviews and employs different strategies in a two-step Pareto selection framework (1-DTC-GL) to overcome these limitations. A detailed experimental study has revealed that existing strategies do not always improve and sometimes even worsen results. Since 1-DTC-GL is a DIRECT-type algorithm, the results of this paper provide general guidance for all DIRECT-type algorithms on how to deal with excessive local refinement more efficiently. [ABSTRACT FROM AUTHOR]

In recent years, there has been a significant increase in the utilization of Tourism Recommendation Systems (TRS) to enhance tourist satisfaction. However, planning a trip can be a daunting and time-consuming process, leading to concerns for travelers. This paper focuses on developing a highly personalized TRS that considers the complexities and limitations of tour itinerary planning. To achieve this, we propose an extension of the constrained orienteering problem that selects the most suitable attractions based on various constraints, such as maximum tour duration, mandatory visits, and start and end locations. In addition, we introduce the use of tier constraints to limit the time spent on similar attractions. In this study, we also propose a novel personalization approach that considers the individual preferences of tourists and generates personalized ratings for points of interest. Next, we focus on developing a new greedy genetic algorithm to address the NP-hard problem of finding optimal or near-optimal solutions. To assess the performance of the developed algorithm, we conducted a sensitivity analysis of the input parameters. Using different user profiles, we demonstrated its effectiveness on a real-world London city dataset. Moreover, we conducted a comparative experimental analysis of the algorithm with four baseline algorithms, and the results were statistically analyzed using non-parametric tests such as Wilcoxon and Friedman. Our algorithm achieved the best-known solutions for 100% of the instances tested, demonstrating its efficiency for large-scale problems with 200-300 points of interest. Furthermore, our TRS provides highly personalized tourist trips, making it a valuable tool for tourists. The new greedy genetic algorithm and unique personalization approach are significant features of the new GlobeTrott tourist recommendation system, which is accessible on popular mobile operating systems and through the website at https://www.globetrott.com/. [ABSTRACT FROM AUTHOR]

In this paper, we present the progress of blockchain technology from the advent of the original publication titled "Bitcoin: A Peer-to-Peer Electronic Cash System," written by the mysterious Satoshi Nakamoto, until the current days. Historical background and a comprehensive overview of the blockchain technology are given. We provide an up-to-date comparison of the most popular blockchain platforms with particular emphasis given to consensus protocols. Additionally, we introduce a BlockLib, an extensively growing online library on blockchain platforms collected from the various sources and designed to enable contributions from the blockchain community. Main directions of the current blockchain research, facing challenges as well as the main fields of applications, are summarized. We also layout the possible future lines in the blockchain technology development. [ABSTRACT FROM AUTHOR]

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 incorporating two-step selection procedure and penalty function approach in our recent DIRECT-GL algorithm. The proposed algorithm effectively explores hyper-rectangles with infeasible centers which are close to boundaries of feasibility and may cover feasible regions. An extensive experimental investigation revealed the potential of the proposed approach compared with other existing DIRECT-type algorithms for constrained global optimization problems, including important engineering problems. [ABSTRACT FROM AUTHOR]

In this article, we present a new open-source tool for algebraic modeling and mathematical optimization. We begin by distilling the main gaps within the existing algebraic modeling languages and tools (varying performance, limited cross-compatibility, complex syntax, and different solver, feature, and problem type support). Later, we propose a state-of-the-art web-based tool (WebAML and Optimization System) for algebraic modeling languages and mathematical optimization. The tool does not require specific algebraic language knowledge, allows solving problems using different solvers, and utilizes the best characteristics of existing algebraic modeling languages. We also provide clear extension points and ideas on how we could further improve such a tool. [ABSTRACT FROM AUTHOR]

Global optimization methods based on Lipschitz bounds have been analyzed and applied widely to solve various optimization problems. In this paper a bound for Lipschitz function is proposed, which is computed using function values at the vertices of a simplex and the radius of the circumscribed sphere. The efficiency of a branch and bound algorithm with proposed bound and combinations of bounds is evaluated experimentally while solving a number of multidimensional test problems for global optimization. The influence of different bounds on the performance of a branch and bound algorithm has been investigated. [ABSTRACT FROM AUTHOR]

The solution of multidimensional Lipschitz optimization problem requires a lot of computing time and memory resources. Parallel OpenMP and MPI versions of branch and bound algorithm with simplicial partitions and Lipschitz bounds were created, investigated and compared in this paper. The efficiency of the developed parallel algorithms is investigated by solving multidimensional test problems for global optimization. [ABSTRACT FROM AUTHOR]

The paper discusses how the used norm and corresponding Lipschitz constant influence the speed of algorithms for global optimization. For this reason Lipschitz constants corresponding to different norms were estimated. Different test functions for global optimization were solved using branch-and-bound algorithm for Lipschitz optimization with different norms. Experiments have shown that the best results are achieved when combination of extreme (infinite and first) and sometimes Euclidean norms is used. [ABSTRACT FROM AUTHOR]

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 results. To promote transparency and trust in research, solutions that improve the accessibility of models and data, facilitate experiment tracking, and allow audit of experimental results are needed. Blockchain technology, characterized by its decentralization, data immutability, cryptographic hash functions, consensus algorithms, robust security measures, access control mechanisms, and innovative smart contracts, offers a compelling pathway for the development of such solutions. To address the reproducibility challenges in machine learning, we present a novel concept of a blockchain-based platform that operates on a peer-to-peer network. This network comprises organizations and researchers actively engaged in machine learning research, seamlessly integrating various machine learning research and development frameworks. To validate the viability of our proposed concept, we implemented a blockchain network using the Hyperledger Fabric infrastructure and conducted experimental simulations in several scenarios to thoroughly evaluate its effectiveness. By fostering transparency and facilitating collaboration, our proposed platform has the potential to significantly improve reproducible research in machine learning and can be adapted to other domains within artificial intelligence. [ABSTRACT FROM AUTHOR]

• A new DIRECT-type derivative-free global optimization method is proposed. • A new hybrid technique unifies a novel partition strategy with sampling on diagonals. • Three local minimization strategies are neatly embedded into the global search. • Comparison on more than 850 test problems shows advantages of the approach. • Results on a real-life nonlinear regression model confirm the goodness of the algorithm. In this paper, black-box global optimization problem with expensive function evaluations is considered. This problem is challenging for numerical methods due to the practical limits on computational budget often required by intelligent systems. For its efficient solution, a new DIRECT-type hybrid technique is proposed. The new algorithm incorporates a novel sampling on diagonals and bisection strategy (instead of a trisection which is commonly used in the existing DIRECT-type algorithms), embedded into the globally-biased framework, and enriched with three different local minimization strategies. The numerical results on a test set of almost 900 problems from the literature and on a real-life application regarding nonlinear regression show that the new approach effectively addresses well-known DIRECT weaknesses, has beneficial effects on the overall performance, and on average, gives significantly better results compared to several DIRECT-type methods widely used in decision-making expert systems. [ABSTRACT FROM AUTHOR]

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 classifications often result in representatives with strongly different characteristics belonging to the same category. To address this challenge, a quantitative data-driven classification methodology that leverages machine learning—specifically, clustering—is introduced here to achieve unbiased grouping of analyzed consensus protocols implemented in various platforms. When different clustering techniques were used on the analyzed DLT dataset, an average consistency of 78 % was achieved, while some instances exhibited a match of 100 % , and the lowest consistency observed was 55 % . [ABSTRACT FROM AUTHOR]

In this work, we perform an extensive theoretical and experimental analysis of the characteristics of five of the most prominent algebraic modelling languages (AMPL, AIMMS, GAMS, JuMP, and Pyomo) and modelling systems supporting them. In our theoretical comparison, we evaluate how the reviewed modern algebraic modelling languages match the current requirements. In the experimental analysis, we use a purpose-built test model library to perform extensive benchmarks. We provide insights on which algebraic modelling languages performed the best and the features that we deem essential in the current mathematical optimization landscape. Finally, we highlight possible future research directions for this work. [ABSTRACT FROM AUTHOR]

Blockchain is one of the most disruptive technologies introduced in Bitcoin, which engaged great attention from the industry and academia and determined a rapid growth of other Distributed Ledger Technologies (DLTs). In the complex architecture of a DLT system, a consensus protocol plays a key role by ensuring that all participants agree on the data integrity without any central authority. A wide range of consensus protocols have been designed with different concepts and properties (e.g., lower energy consumption, better scalability, smaller latency, higher throughput, etc.). The key requirements for consensus protocols passing from one blockchain system to another often differ significantly, and there is no one-fit-all protocol. Therefore, selecting the most suitable consensus protocol for a particular DLT system is essential, but at the same time a challenging step, as decision-makers need to make a trade-off between conflicting requirements. This paper introduces a framework for selecting the most suitable consensus protocols depending on the identified criteria, priorities, and other requirements by incorporating Multi-Criteria Decision-Making (MCDM) techniques. We demonstrate its potential by identifying the preferable consensuses for the three most common types of existing blockchain systems and on an actual application for bike renting. Moreover, the collected data and tools are freely available, ensuring full replicability, reusability, and further development. • A comprehensive literature review dedicated to selecting consensus protocols. • A new open-source data collection reflecting 18 state-of-the-art consensus protocols. • The first MCDM-based framework to identify preferable consensus protocols. • Demonstration on three main types of blockchains and bike renting application. [ABSTRACT FROM AUTHOR]

• The first deterministic non-redundant parallel DIRECT-type algorithms for generally constrained global optimization are introduced. • The introduced load balancing scheme for the master-worker parallel implementation can be easily adapted for other DIRECT-type algorithms. • Dynamic data structures are included in the sequential DIRECT-GLce algorithm, resulting in one of the most efficient DIRECT-type methods. • The created dataset of generally constrained global optimization problems and developed source codes of the algorithms are freely available. 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, and a distributed parallel version for the aggressive counterpart. The efficiency of DIRECT-type parallel versions is evaluated solving box- and generally constrained global optimizations problems with varying complexity, including a practical NASA speed reducer design problem. Numerical results show a good efficiency, especially for the distributed parallel version of the original DIRECT-GLce on a multi-core PC. [ABSTRACT FROM AUTHOR]