Automatic computing has undergone drastic changes since the pioneering days of the early Fifties, one of the most obvious being that today the majority of computer programs are no longer written in machine code but in some programming language like FORTRAN or ALGOL. However, as desirable as the time-saving achieved in this way may be, still a high proportion of the preparatory work must be attributed to activities such as error estimates, stability investigations and the like, and for these no programming aid whatsoever can be of help. In this respect, ALGOL, as an internationally standardized notation which avoids computer-oriented concepts, provides another advantage, not often mentioned, but one which was already the guiding principle at the very beginning of the programming language venture: indeed, a correct ALGOL program is the abstractum of a computing process for which the necessary analyses have already been performed. It is the very purpose of this Handbook to establish such abstract formulations of certain computing processes. Therefore, numerical methods given in this Hand­ book in the form of ALGOL procedures may be put to immediate use wherever ALGOL is known and understood; in fact, application of such a method reduces to little more than calling the corresponding procedure.

Problem oriented programming languages as they have developed over the last ten years essentially serve two purposes which somewhat crudely can be described by the terms man-man communication and man-machine communication, respectively. As a carrier of information between humans, the problem oriented programming language is designed to express the essence of an algorithm in a way which is un­ ambiguous and concise as well as independent of (and therefore meaning­ ful without any reference to) the changing details of computing machine­ ry. As a carrier of information from man to computer, the language permits the human programmer to express his computational needs in a compact way adapted to the general characteristics of computers, but freed from the burdening details of specific computer facilities. This presupposes the existence of algorithms, or programs, which permit the computer itself to transform efficiently programs written in the problem oriented language into machine programs. Thus the entire computing community profits from the work of the individual programmer. The primary purpose of the Handbook is to present a set of algorithms of broad utility from the domain of numerical mathematics written in the problem oriented language ALGOL 60. Therefore, volumes I a and I b are in a sense supplementary as they serve to introduce this language. Volume I a gives a description of the language proper and of its use for writing correct programs. Thus, volume I a primarily covers the aspect of man-man communication by means of ALGOL 60.

Empirical research often lacks theory. This book progressively works out a method of constructing models which can bridge the gap between empirical and theoretical research in the social sciences. This might improve the explanatory power of models. The issue is quite novel, and it benefited from a thorough examination of statistical and mathematical models, conceptual models, diagrams and maps, machines, computer simulations, and artificial neural networks. These modelling practices have been approached through different disciplines.

"The efficient utilization of energy, sustainable use of natural resources, and large-scale adoption of sustainable technologies is the key to a sustainable future. The Handbook of Sustainable Engineering provides tools that will help us achieve these goals".

Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics – trial-and-error search and delayed reward – are the most important distinguishing features of reinforcement learning.

Karen M. McManus est une auteure pour jeunes adultes.
"Qui ment ?" ("One of Us Is Lying", 2017), son premier roman, a été traduit en plusieurs langues. Il a été adapté en série télévisée, diffusée sur Netflix en février 2022.
La suite, "Qui meurt ?" ("One of Us is Next", 2020), a également été un succès. "Qui tue ?" ("One of Us Is Back", 2023), traduit en français en 2024, clôture la trilogie. …