scholarly journals A characterization of optimal multiprocessor schedules and new dominance rules

2020 ◽  
Vol 40 (4) ◽  
pp. 876-900
Author(s):  
Rico Walter ◽  
Alexander Lawrinenko
Keyword(s):  
Parallel Machines ◽  
Computational Study ◽  
Search Space ◽  
Point Of View ◽  
Theoretical Point ◽  
Optimal Solutions ◽  
Identical Parallel Machines ◽  
Makespan Minimization ◽  
Structural Pattern

Abstract The paper on hand approaches the classical makespan minimization problem on identical parallel machines from a rather theoretical point of view. Using an approach similar to the idea behind inverse optimization, we identify a general structural pattern of optimal multiprocessor schedules. We also show how to derive new dominance rules from the characteristics of optimal solutions. Results of our computational study attest to the efficacy of the new rules. They are particularly useful in limiting the search space when each machine processes only a few jobs on average.

scholarly journals Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks

2019 ◽  
Vol 33 ◽  
pp. 4602-4609 ◽  
Author(s):  
Christopher Morris ◽  
Martin Ritzert ◽  
Matthias Fey ◽  
William L. Hamilton ◽  
Jan Eric Lenssen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Multiple Scales ◽  
Graph Isomorphism ◽  
Higher Order ◽  
Point Of View ◽  
Theoretical Point ◽  
Graph Classification ◽  
Graph Neural Networks ◽  
Vector Representations

In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion. Up to now, GNNs have only been evaluated empirically—showing promising results. The following work investigates GNNs from a theoretical point of view and relates them to the 1-dimensional Weisfeiler-Leman graph isomorphism heuristic (1-WL). We show that GNNs have the same expressiveness as the 1-WL in terms of distinguishing non-isomorphic (sub-)graphs. Hence, both algorithms also have the same shortcomings. Based on this, we propose a generalization of GNNs, so-called k-dimensional GNNs (k-GNNs), which can take higher-order graph structures at multiple scales into account. These higher-order structures play an essential role in the characterization of social networks and molecule graphs. Our experimental evaluation confirms our theoretical findings as well as confirms that higher-order information is useful in the task of graph classification and regression.

Why Measuring Inequality by the Variance Makes Sense from a Theoretical Point of View

2001 ◽  
Author(s):  
Satya R. Chakravarty
Keyword(s):  
Quadratic Form ◽  
Quadratic Forms ◽  
Point Of View ◽  
Theoretical Point ◽  
Numerical Illustration ◽  
Absolute Inequality

The variance, which has many advantages as a measure of inequality, is a quadratic form in incomes. In this paper we develop a characterization of the variance by showing that among all quadratic forms in incomes, it is the only one that satisfies absolute inequality invariance, symmetry, the principle of transfers and the principle of population. A numerical illustration of several inequality indices, including the variance, is also presented in the paper.

Usage of Metaheuristics in Engineering

2013 ◽  
pp. 484-528 ◽  
Author(s):  
Ozlem Senvar ◽  
Ebru Turanoglu ◽  
Cengiz Kahraman
Keyword(s):  
Learning Strategies ◽  
Search Space ◽  
Point Of View ◽  
Generation Process ◽  
Optimal Solutions ◽  
Future Directions ◽  
Structure Information ◽  
Advantages And Disadvantages ◽  
Engineering Problems ◽  
Metaheuristic Techniques

A metaheuristic is conventionally described as an iterative generation process which guides a servient heuristic by combining intelligently different concepts for exploring and exploiting the search space, learning strategies are used to structure information in order to find efficiently near-optimal solutions. In the literature, usage of metaheuristic in engineering problems is increasing in a rapid manner. In this study; a survey of the most important metaheuristics from a conceptual point of view is given. Background knowledge for each metaheuristics is presented. The publications are classified with respect to the used metaheuristic techniques and application areas. Advantages and disadvantages of metaheuristics can be found in this chapter. Future directions of metaheuristics are also mentioned.

Noun Phrases

2019 ◽  
pp. 334-357
Author(s):  
Evelien Keizer
Keyword(s):  
Internal Structure ◽  
Noun Phrase ◽  
Noun Phrases ◽  
Point Of View ◽  
Theoretical Point ◽  
Cognitive Perspective ◽  
General Characterization ◽  
Theoretical Approaches ◽  
English Noun

This chapter provides a brief overview of some widely debated issues in discussions of the English noun phrase, and illustrates how these issues have been dealt with in different theoretical approaches. After a general characterization of the noun phrase from a pre-theoretical point of view, the chapter proceeds to discuss the internal structure of the noun phrase from a generative, functional, and cognitive perspective. Subsequently, the differences between these approaches are illustrated by addressing two basic notions in the analysis of English noun phrases: headedness (in regular noun phrases, as well as in headless and pseudo-partitive noun phrases) and the distinction between relational and non-relational nouns (and, consequently, between complements and modifiers). In both cases the various types of criteria for analysis are discussed, as well as some problems in applying these criteria.

Localization in Wireless Sensor Networks: Solvability Improvement Technique Using Priori Information from Sensing Data and Network Properties in Unit Disk Graph Model

2014 ◽  
Vol 931-932 ◽  
pp. 999-1003
Author(s):  
Phisan Kaewprapha
Keyword(s):  
Unique Solution ◽  
Graph Model ◽  
Search Space ◽  
Point Of View ◽  
Theoretical Point ◽  
Network Graph ◽  
Free Position ◽  
Sparse Networks ◽  
Priori Information ◽  
Priori Knowledge

From the theoretical point of view, network localization can be viewed as finding a unique solution from distances constraint among points. The one of the difficulties is that even if the network is uniquely localizable, it is proven to be an NP-Hard [1]. It is also true that the network graph has to be sufficiently dense [2]. This poses even more challenges to the original problem as we often work on sparse networks. To cope with this, in [3], we introduce priori knowledge to assist the process of finding the unique localization solution. It helps to speed up the searching algorithm; however, the ambiguity still exists among sparse networks. In this paper we try to bring as much priori knowledge as possible to assist or to be used as constraints. Hopefully this will reduce search space and reach the unique solution quickly. In clean environment, this extra info will, by some magnitude, bring the graph closer to the unique answer. We start from integer-coordinate noise-free position and then add sources of priori knowledge. Then we examine the case where assisted data can be noisy. A search is used within the noisy but useful constraint. The justification of using the assisted knowledge is from the practical uses of some networks, e.g. sensor network, where other measurements are available and they are often correlated and can be helpful in determining the positions.

scholarly journals Challenges for the Measurement of Innovation Ecosystems and Entrepreneurial Ecosystems in Brazil

2020 ◽  
Author(s):  
Renata Lèbre La Rovere ◽  
Guilherme de Oliveira Santos ◽  
Bianca Louzada Xavier Vasconcellos
Keyword(s):  
Small Businesses ◽  
Time Lag ◽  
Point Of View ◽  
Data Sources ◽  
Theoretical Point ◽  
Entrepreneurial Ecosystems ◽  
Innovation Ecosystems ◽  
Multiple Data ◽  
Brazilian Case

Purpose: This paper aims to identify metrics and indicators of innovation ecosystems and entrepreneurial ecosystems and to discuss the limitations of these metrics in the Brazilian case. Theoretical framework: From a theoretical point of view, the paper contributes to the analysis of the differences and similarities between the concepts of innovation ecosystems and entrepreneurial ecosystems. From a methodological perspective, the paper proposes indicators and metrics and points out the limitations for measuring entrepreneurial and innovative ecosystems in Brazil. Design/methodology/approach: The study’s qualitative approach is based on a literature review, a documentary research, and data collection for the characterization of innovation ecosystems and entrepreneurial ecosystems. The paper identifies the main indicators and metrics, their data sources and the limitations of these indicators and metrics in the Brazilian case. Findings: It was observed that despite the existence of multiple data sources, the measurement of entrepreneurial ecosystems in Brazil entails constraints such as time lag of the data; voluntary filling of databases; lack of transparency at the regional level; and incomplete or skewed data. Research, Practical & Social implications: From a theoretical point of view, the paper contributes to the analysis of the differences and similarities between the concepts of innovation ecosystems and entrepreneurial ecosystems. From a methodological point of view, the study proposes indicators and metrics and points out the limitations for the measurement of entrepreneurial and innovative ecosystems in Brazil. Originality/value: When identifying limitations, the paper proposes alternatives to improve the measurement of innovation ecosystems and entrepreneurial ecosystems in the country and in its different regions. This is essential for designing and monitoring public policies to support innovation, especially those aimed to support entrepreneurs and small businesses.

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