Editorial Optimal Control: Theory and Application to Science, Engineering, and Social Sciences Davide La Torre, 1,2 Herb Kunze, 3 Manuel Ruiz-Galan, 4 Tufail Malik, 1 and Simone Marsiglio 5 1 Department of Applied Mathematics and Sciences, Khalifa University, Abu Dhabi 127788, UAE 2 Department of Economics, Management, and Quantitative Methods, University of Milan, 20122 Milan, Italy 3 Department of Mathematics and Statistics, University of Guelph, Guelph, ON, Canada N1G 2W1 4 Department of Applied Mathematics, University of Granada, 18071 Granada, Spain 5 School of Accounting, Economics and Finance, University of Wollongong, Wollongong, NSW 2522, Australia Correspondence should be addressed to Davide La Torre; davide.latorre@kustar.ac.ae Received 12 March 2015; Accepted 12 March 2015 Copyright © 2015 Davide La Torre et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. An optimal control problem entails the identiication of a fea- sible scheme, policy, program, strategy, or campaign, in order to achieve the optimal possible outcome of a system. More formally, an optimal control problem means endogenously controlling a parameter in a mathematical model to produce an optimal output, using some optimization technique. he problem comprises an objective (or cost) functional, which is a function of the state and control variables, and a set of constraints. he problem seeks to optimize the objective function subject to the constraints construed by the model describing the evolution of the underlying system. he two popular solution techniques of an optimal control problem are Pontryagin’s maximum principle and the Hamilton- Jacobi-Bellman equation [1]. Optimal control has become a highly established research front in recent years with numerous contributions to the theory, in both deterministic and stochastic contexts. Its application to diverse ields such as biology, economics, ecology, engineering, inance, management, and medicine cannot be overlooked (see, e.g., [1–12]). he associated mathematical models are formulated, for example, as systems of ordinary, partial, or stochastic diferential equations or discrete dynamical systems, for both scalar and multicriteria decision-making contexts. his special issue is aimed at creating a multidisciplinary forum of discussion on recent advances in theory as well as applications. In response to the call for papers, 58 papers were received. Ater a rigorous refereeing process, 7 papers were accepted for publication in this special issue. he articles included in the issue cover novel contributions to optimal control theory as well as a broad spectrum of applications of optimal control from inance to resource management to engineering to marketing. he paper by Z. Lu and X. Huang investigates discretiza- tion of general linear hyperbolic optimal control problems and derives a priori error estimates for mixed inite element approximation of such optimal control problems. he paper by R. Wu et al. proposes, analyzes, and solves an optimal control exploiting model of renewable resources based on efective utilization rate and illustrates its solution numerically. he paper by N. Zulkarnain et al. compares the perfor- mance of an active antiroll bar system in an automobile using two control models, namely, the linear quadratic Gaussian and the composite nonlinear feedback controller. here are three works focusing on applications in eco- nomics and inance. he paper by J. Ma et al. studies the dynamics of an oligopoly model considering the output of an upstream monopoly and prices of two downstream oligopolies; it is shown that the long-run average proits of the three irms are optimal in the region of stability of the Nash equilibrium point. he paper by D.-L. Sheng et al. uses the Hindawi Publishing Corporation Abstract and Applied Analysis Volume 2015, Article ID 890527, 2 pages http://dx.doi.org/10.1155/2015/890527