The American Journal of Algorithms and Computing aims to publish research and review articles of the highest quality on advanced computational algorithms and their application domains. One objective of the journal is to facilitate communication between researchers in applied mathematics and in computer sciences, who are concerned with the design and the performance analysis of algorithms. The journal welcome papers on stochastic algorithms, advanced Monte Carlo methods, bayesian inference, Markov chains analysis, optimization and optimal control, risk analysis, calibration and uncertainty propagations, statistical machine learning, queueing processes, networks analysis, as well as mathematical programming, combinatorics, graphs algorithms, parallel computing and distributed systems techniques.
The purpose of the journal is to feature new algorithms with high applicable potential, or new analyses of known algorithms. The journal welcomes articles on mathematical foundations and performance analysis studies, as well as more applied articles on computational issues and currently-active subject areas, including information theory, signal processing and filtering, robotics, biology, medicine, epidemiology, molecular chemistry, as well as financial mathematics, and econometrics. The journal is pledge to maintain high quality standards, with transparent and minimal reviewing delays.
This journal presents papers that embrace a wide diversity of applied areas, such as physical, chemical, biochemical, environmental topics, and optimum design, as well as stochastic finance. It includes the theories for solving problems, in addition to deterministic and stochastic optimization: partial differential equations dealing with applied problems, such as turbulence, homogenization, and stochastic differential equations occurring in filtering theory and in biology. The journal also features critical surveys of new advances in theory and application.
The journal publishes original research papers of high scientific quality in two areas: Mathematical Modelling, and Numerical Analysis. Mathematical Modelling comprises the development and study - e.g. structure, well-posedness, solution properties - of a mathematical formulation of a problem (or class of problems). Numerical Analysis comprises the formulation and study - e.g. stability, convergence, computational complexity - of a numerical approximation or solution approach to a mathematically formulated problem (or class of problems).
Stochastic Processes and their Applications publishes papers on the theory and applications of stochastic processes. It is concerned with concepts and techniques, and is oriented towards a broad spectrum of mathematical, scientific and engineering interests.
Characterization, structural properties, inference and control of stochastic processes are covered. The journal is exacting and scholarly in its standards. Every effort is made to promote innovation, vitality, and communication between disciplines.
Stochastic Analysis and Applications presents the latest innovations in the field of stochastic theory and its practical applications, as well as the full range of related approaches to analyzing systems under random excitation. In addition, it is the only publication that offers the broad, detailed coverage necessary for the interfield and intrafield fertilization of new concepts and ideas, providing the scientific community with a unique and highly useful service.
This journal is part of the European Series in Applied and Industrial Mathematics (ESAIM).
Published under the scientific responsibility of the Société de Mathématiques Appliquées et Industrielles (SMAI)
and with the support of the Centre National de la Recherche Scientifique (CNRS)