Although many methods are there for solving nonlinear optimization problems, effective constraint handling still remains as a big challenge. Most methods use exterior penalty function like schemes for it, which requir...
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Oxygen starvation is one of the key technical challenges in proton exchange membrane (PEM) fuel cells operation. It impacts the system's durability, performance, and safety, especially under severe variability of ...
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This paper introduces a traffic evacuation model for railway disruptions to improve resilience. The research focuses on the problem of failure of several nodes or lines on the railway network topology. We proposed a h...
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Mathematical programming approaches, such as Lagrangian relaxation, have the advantage of computational efficiency when the optimization problems are decomposable. Lagrangian relaxation belongs to a class of primal-du...
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ISBN:
(纸本)9781467330374;9781467330367
Mathematical programming approaches, such as Lagrangian relaxation, have the advantage of computational efficiency when the optimization problems are decomposable. Lagrangian relaxation belongs to a class of primal-dual algorithms. Subgradient-based optimization methods can be used to optimize the dual functions in Lagrangian relaxation. In this paper, three subgradient-based methods, the subgradient (SG), the surrogate subgradient (SSG) and the surrogate modified subgradient (SMSG), are adopted to solve a demonstrative nonlinear programming problem to assess the performances on optimality in order to demonstrate its applicability to the realistic problem.
This paper proposes a comprehensive approach to improve the computational efficiency of Reinforcement Learning (RL) based Model Predictive Controller (MPC). Although MPC will ensure controller safety and RL can genera...
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This paper proposes a comprehensive approach to improve the computational efficiency of Reinforcement Learning (RL) based Model Predictive Controller (MPC). Although MPC will ensure controller safety and RL can generate optimal control policies, combining the two requires substantial time and computational effort, particularly for larger data sets. In a typical RL-based MPC and Q- learning workflow, two not-so-different MPC problems must be evaluated at each RL iteration, i.e. one for the action-value and one for the value function, which is time-consuming and prohibitively expensive in terms of computations. We employ nonlinear programming (NLP) sensitivities to approximate the action-value function using the optimal solution from the value function, reducing computational time. The proposed approach can achieve comparable performance to the conventional method but with significantly lower computational time. We demonstrate the proposed approach on two examples: Linear Quadratic Regulator (LQR) problem and Continuously Stirred Tank Reactor (CSTR).
A modified form of Legendre-Gauss orthogonal direct collocation is developed for solving optimal control problems whose solutions are nonsmooth due to control discontinuities. This new method adds switch-time variable...
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We propose a novel method for spatiotemporal multi-camera calibration using freely moving people in multiview videos. Since calibrating multiple cameras and finding matches across their views are inherently interdepen...
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Metaheuristic algorithms are advanced optimization techniques widely used to solve complex, large-scale, and nonlinear optimization problems where conventional methods struggle. Unlike traditional algorithms, which re...
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Terahertz (THz) communication is a promising technology for future wireless communications, offering data rates of up to several terabits-per-second (Tbps). However, the range of THz band communications is often limit...
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We present a new framework for the simultaneous optimiziation of both the topology as well as the relative density grading of cellular structures and materials, also known as lattices. Due to manufacturing constraints...
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