An evolutionary approach to passive learning in optimal control problems

Unidad de excelencia María de Maeztu CEX2019-000940-MWe consider the optimal control problem of a small nonlinear econometric model under parameter uncertainty and passive learning (open-loop feedback). Traditionally, this type of problems has been approached by applying linear-quadratic optimization algorithms. However, the literature demonstrated that those methods are very sensitive to the choice of random seeds frequently producing very large objective function values (outliers). Furthermore, to apply those established methods, the original nonlinear problem must be linearized first, which runs the risk of solving already a different problem. Following Savin and Blueschke (Comput Econ 48(2):317-338, 2016) in explicitly addressing parameter uncertainty with a large Monte Carlo experiment of possible parameter realizations and optimizing it with the Differential Evolution algorithm, we extend this approach to the case of passive learning. Our approach provides more robust results demonstrating greater benefit from learning, while at the same time does not require to modify the original nonlinear problem at hand. This result opens new avenues for application of heuristic optimization methods to learning strategies in optimal control research

Deep Learning networks with p-norm loss layers for spatial resolution enhancement of 3D medical images

Thurnhofer-Hemsi K., López-Rubio E., Roé-Vellvé N., Molina-Cabello M.A. (2019) Deep Learning Networks with p-norm Loss Layers for Spatial Resolution Enhancement of 3D Medical Images. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science, vol 11487. Springer, ChamNowadays, obtaining high-quality magnetic resonance (MR) images is a complex problem due to several acquisition factors, but is crucial in order to perform good diagnostics. The enhancement of the resolution is a typical procedure applied after the image generation. State-of-the-art works gather a large variety of methods for super-resolution (SR), among which deep learning has become very popular during the last years. Most of the SR deep-learning methods are based on the min- imization of the residuals by the use of Euclidean loss layers. In this paper, we propose an SR model based on the use of a p-norm loss layer to improve the learning process and obtain a better high-resolution (HR) image. This method was implemented using a three-dimensional convolutional neural network (CNN), and tested for several norms in order to determine the most robust t. The proposed methodology was trained and tested with sets of MR structural T1-weighted images and showed better outcomes quantitatively, in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the restored and the calculated residual images showed better CNN outputs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Analysis and Projection of Transport Sector Demand for Energy and Carbon Emission: An Application of the Grey Model in Pakistan

The incredible increase in carbon emissions is a major global concern. Thus, academicians and policymakers at COP26 are continuously urging to devise strategies to reduce carbon and other greenhouse gas emissions. The transportation sector is a major contributor to greenhouse gas emissions in developing countries. Therefore, this study projected an increase in fossil fuel demand for transportation and corresponding carbon dioxide emission in Pakistan from 2018 to 2030 by employing the Grey model and using annual data from 2010 to 2018. Furthermore, the determinant of fossil fuel demand is modeled using an environmental sustainability model such as stochastic regression IPAT that links environmental impact as a product of population, affluence, and technology on annual time series data spanning from 1990 to 2019. The projected values of oil demand and carbon emissions reveal an increasing trend, with average annual growth rates of 12.68% and 11.45%, respectively. The fully modified ordinary least squares (FM-OLS) findings confirmed the environmental Kuznets hypothesis. The increase in population growth emerged as the major driver for oil demand and carbon dioxide emissions, while technological advancement can reduce oil demand and corresponding carbon emissions. This study urges Pakistan to switch from oil to gas and other green energies by encouraging hybrid vehicles, as the number of vehicles on the road positively impacts the transport sector’s oil demand. Moreover, increasing economic growth and controlling the population growth rate by discouraging more children can be a valid policy for reducing oil demand and corresponding carbon emissions. © 2023 by the authors.Ministry of Education and Science of the Russian Federation, MinobrnaukaThe research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority-2030 Program) is gratefully acknowledged

Absorptive capacity and innovation: When is it better to cooperate?

Cooperation can benefit and hurt firms at the same time. An important question then is: when is it better to cooperate? And, once the decision to cooperate is made, how can an appropriate partner be selected? In this paper we present a model of inter-firm cooperation driven by cognitive distance, appropriability conditions and external knowledge. Absorptive capacity of firms develops as an outcome of the interaction between absorptive R&D and cognitive distance from voluntary and involuntary knowledge spillovers. Thus, we offer a revision of the original model by Cohen and Levinthal (Econ J 99(397):569-596, 1989), accounting for recent empirical findings and explicitly modeling absorptive capacity within the framework of interactive learning. We apply that to the analysis of firms' cooperation and R&D investment preferences. The results show that cognitive distance and appropriability conditions between a firm and its cooperation partner have an ambiguous effect on the profit generated by the firm. Thus, a firm chooses to cooperate and selects a partner conditional on the investments in absorptive capacity it is willing to make to solve the understandability/novelty trade-off. © 2014 Springer-Verlag Berlin Heidelberg

An Evolutionary Approach to Passive Learning in Optimal Control Problems

AbstractWe consider the optimal control problem of a small nonlinear econometric model under parameter uncertainty and passive learning (open-loop feedback). Traditionally, this type of problems has been approached by applying linear-quadratic optimization algorithms. However, the literature demonstrated that those methods are very sensitive to the choice of random seeds frequently producing very large objective function values (outliers). Furthermore, to apply those established methods, the original nonlinear problem must be linearized first, which runs the risk of solving already a different problem. Following Savin and Blueschke (Comput Econ 48(2):317–338, 2016) in explicitly addressing parameter uncertainty with a large Monte Carlo experiment of possible parameter realizations and optimizing it with the Differential Evolution algorithm, we extend this approach to the case of passive learning. Our approach provides more robust results demonstrating greater benefit from learning, while at the same time does not require to modify the original nonlinear problem at hand. This result opens new avenues for application of heuristic optimization methods to learning strategies in optimal control research.</jats:p

OPTCON3: An Active Learning Control Algorithm for Nonlinear Quadratic Stochastic Problems

AbstractIn this paper, we describe the new OPTCON3 algorithm, which serves to determine approximately optimal policies for stochastic control problems with a quadratic objective function and nonlinear dynamic models. It includes active learning and the dual effect of optimizing policies, whereby optimal policies are used to learn about the stochastics of the dynamic system in addition to their immediate effect on the performance of the system. The OPTCON3 algorithm approximates the nonlinear model with a time-varying linear model and applies a procedure similar to that of Kendrick to the series of linearized models to calculate approximately optimal policies. The results for two simple economic models serve to test the OPTCON3 algorithm and compare it to previous solutions of the stochastic control problem. Initial evaluations show that the OPTCON3 approach may be promising to enhance our understanding of the adaptive economic policy problem under uncertainty.</jats:p