Submission deadline: 2023-12-31
Section Collection Editors

Section Collection Information

Dear Esteemed Colleagues,

The Journal of Applied Match covers a broad range of mathematical methods and this section amplifies optimization with some applications. Papers considered for publication must contain significant contributions and applications from a mathematical perspective. Core topics include optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Eastern, analytical threads of work following the methods of Lev Pontryagin, taken together with theories of William Karush, Harold Kuhn, and Albert Tucker are particularly welcome in addition to Western methods akin Richard E. Bellman’s numerical methods, amongst others. Algorithmic, data analytic, machine learning and numerical methods are also encouraged. Novel ideas should contain some potential within science and engineering.

 In an era where the number of transistors on a microchip is assumed to double every two years an expectation exists for speed and capability of computers to increase every two years while cost will continually decrease. Such growth is often assumed to be exponential leading to a present-day, arguably hyper-emphasized numerical optimization coincident with de-emphasized analytic optimization. Thus, this section seeks manuscripts espousing either approach: analytical or numerical, and particularly welcome manuscripts comparing the two.

We look forward to receiving your contributions.

Prof. Dr. Timothy Sands
Prof. Dr. Stojan Novica Radenovic
Prof. Dr. Hasan Koten
Prof. Dr. Salih Berkan Aydemir
Prof. Dr. Ryspek Usubamatov
Prof. Dr. Muhammad Nadeem
Prof. Dr. Donatella Granata
Prof. Dr. Alexander Bochkov
Prof. Dr. Neal Xiong
Section Editors


Minimization; Adjoint; Co-state; Lagrange Multiplier; Convector; Dual; Hamiltonian; Lagrangian; Terminal Transversality; Evolution; Final Value; Bellman’s Principle; Liner/Nonlinear Programming; Convex Optimization; Gradient Descent; Constrained Optimization; Integer Programming; Stochastic Optimization; Interior Point Methods; Global Optimization.

Published Paper