One-Dimensional Optimization Problem

2020 ◽  
pp. 1-14
Author(s):  
Nita H. Shah ◽  
Poonam Prakash Mishra
Keyword(s):  
Optimization Problem ◽  
One Dimensional ◽  
Dimensional Optimization

scholarly journals Collaborative Policy of the Supply-Hub for Assemble-to-Order Systems with Delivery Uncertainty

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Guo Li ◽  
Mengqi Liu ◽  
Xu Guan ◽  
Zheng Huang
Keyword(s):  
Numerical Analysis ◽  
Optimization Problem ◽  
Service Level ◽  
Original Model ◽  
Order System ◽  
One Dimensional ◽  
Two Factors ◽  
Increasing Function ◽  
Optimal Assembly ◽  
Dimensional Optimization

This paper considers the collaborative mechanisms of the Supply-Hub in the Assemble-to-Order system (ATO system hereafter) with upstream delivery uncertainty. We first propose a collaborative replenishment mechanism in the ATO system, and construct a replenishment model with delivery uncertainty in use of the Supply-Hub. After transforming the original model into a one-dimensional optimization problem, we derive the optimal assembly quantity and reorder point of each component. In order to enable the Supply-Hub to conduct collaborative replenishment with each supplier, the punishment and reward mechanisms are proposed. The numerical analysis illustrates that service level of the Supply-Hub is an increasing function of both punishment and reward factors. Therefore, by adjusting the two factors, suppliers’ incentives of collaborative replenishment can be significantly enhanced, and then the service level of whole ATO system can be improved.

One-Dimensional Optimization Problem

2020 ◽  
pp. 1-14
Author(s):  
Nita H. Shah ◽  
Poonam Prakash Mishra
Keyword(s):  
Optimization Problem ◽  
One Dimensional ◽  
Dimensional Optimization

scholarly journals Optimal Unimodular Sequences Design Method for Active Sensing Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Ze Li ◽  
Ping Li ◽  
Xinhong Hao ◽  
Xiaopeng Yan
Keyword(s):  
Optimization Problems ◽  
Design Method ◽  
Correlation Coefficients ◽  
Continuous Phase ◽  
Active Sensing ◽  
Sidelobe Level ◽  
One Dimensional ◽  
Sensing Systems ◽  
Practical Engineering ◽  
Dimensional Optimization

In active sensing systems, unimodular sequences with low autocorrelation sidelobes are widely adopted as modulation sequences to improve the distance resolution and antijamming performance. In this paper, in order to meet the requirements of specific practical engineering applications such as suppressing certain correlation coefficients and finite phase, we propose a new algorithm to design both continuous phase and finite phase unimodular sequences with a low periodic weighted integrated sidelobe level (WISL). With the help of the transformation matrix, such an algorithm decomposes the N-dimensional optimization problem into N one-dimensional optimization problems and then uses the iterative method to search the optimal solutions of the N one-dimensional optimization problems directly. Numerical experiments demonstrate the effectiveness and the convergence property of the proposed algorithm.

scholarly journals Heat transfer from convecting-radiating fin through optimized Chebyshev polynomials with interior point algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 102-110
Author(s):  
Elyas Shivanian ◽  
Mahdi Keshtkar ◽  
Hamidreza Navidi
Keyword(s):  
Heat Transfer ◽  
Chebyshev Polynomials ◽  
Interior Point ◽  
Interior Point Method ◽  
Optimization Problem ◽  
Mathematical Formulation ◽  
Interior Point Algorithm ◽  
Equivalent Problem ◽  
One Dimensional ◽  
Fin Efficiency

AbstractIn this paper, the problem of determining heat transfer from convecting-radiating fin of triangular and concave parabolic shapes is investigated.We consider one-dimensional, steady conduction in the fin and neglect radiative exchange between adjacent fins and between the fin and its primary surface. A novel intelligent computational approach is developed for searching the solution. In order to achieve this aim, the governing equation is transformed into an equivalent problem whose boundary conditions are such that they are convenient to apply reformed version of Chebyshev polynomials of the first kind. These Chebyshev polynomials based functions construct approximate series solution with unknown weights. The mathematical formulation of optimization problem consists of an unsupervised error which is minimized by tuning weights via interior point method. The trial approximate solution is validated by imposing tolerance constrained into optimization problem. Additionally, heat transfer rate and the fin efficiency are reported.

Burn phase calculations and one-dimensional optimization studies for an inversez-pinch magnetized target fusion system

2011 ◽  
Vol 53 (8) ◽  
pp. 085013
Author(s):  
P V Subhash ◽  
S Madhavan ◽  
N Sakthivel ◽  
V Mishra ◽  
Aaditya V Majalee ◽  
...  
Keyword(s):  
Fusion System ◽  
Magnetized Target Fusion ◽  
One Dimensional ◽  
Dimensional Optimization

scholarly journals Empirical Investigation of Optimization Algorithms in Neural Machine Translation

2017 ◽  
Vol 108 (1) ◽  
pp. 13-25 ◽  
Author(s):  
Parnia Bahar ◽  
Tamer Alkhouli ◽  
Jan-Thorsten Peter ◽  
Christopher Jan-Steffen Brix ◽  
Hermann Ney
Keyword(s):  
Neural Networks ◽  
Machine Translation ◽  
Optimization Problem ◽  
Empirical Investigation ◽  
State Of The Art ◽  
Optimization Techniques ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
And Training ◽  
Dimensional Optimization

AbstractTraining neural networks is a non-convex and a high-dimensional optimization problem. In this paper, we provide a comparative study of the most popular stochastic optimization techniques used to train neural networks. We evaluate the methods in terms of convergence speed, translation quality, and training stability. In addition, we investigate combinations that seek to improve optimization in terms of these aspects. We train state-of-the-art attention-based models and apply them to perform neural machine translation. We demonstrate our results on two tasks: WMT 2016 En→Ro and WMT 2015 De→En.

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