scholarly journals Development of a Gradient Descent Algorithm for Pathway Routing Based on Functional-Voxel Modeling

2021 ◽  
Author(s):  
Alexey Tolok ◽  
Pavel Petukhov
Keyword(s):  
Gradient Method ◽  
Gradient Descent ◽  
Mathematical Apparatus ◽  
Basic Principles ◽  
Color Palette ◽  
Gradient Descent Algorithm ◽  
Target Control ◽  
Voxel Model ◽  
Solution Surface ◽  
R Functions

This paper considers a pathfinding algorithm using the gradient method for functional-voxel modeling problems. The basic principles of constructing gradient lines based on the functional voxel model are investigated. The tool to describe the scene with obstacles is the mathematical apparatus of R-functions. To solve the problem of pathfinding in a weakly deterministic environment, we propose an algorithm that is based on the use of: gradient method analyzing the color palette of images of local features of the function; mathematical apparatus of R- functions describing the topography of the solution surface at the current moment in time. An algorithm of target control that solves the problem of getting out of possible "trapped objects" is considered. For this, the principle of changing the position of the target was developed to control the gradient direction.

scholarly journals Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm

2021 ◽  
Author(s):  
Marco Mele ◽  
Cosimo Magazzino ◽  
Nicolas Schneider ◽  
Floriana Nicolai
Keyword(s):  
Economic Growth ◽  
Gradient Descent ◽  
Statistical Data ◽  
Stochastic Gradient Descent ◽  
Policy Recommendations ◽  
Dynamic Interactions ◽  
Gradient Descent Algorithm ◽  
The Relationship ◽  
Main Strand ◽  
Yearly Data

AbstractAlthough the literature on the relationship between economic growth and CO2 emissions is extensive, the use of machine learning (ML) tools remains seminal. In this paper, we assess this nexus for Italy using innovative algorithms, with yearly data for the 1960–2017 period. We develop three distinct models: the batch gradient descent (BGD), the stochastic gradient descent (SGD), and the multilayer perceptron (MLP). Despite the phase of low Italian economic growth, results reveal that CO2 emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO2 increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided.

scholarly journals Adaptive Gradient Estimation Stochastic Parallel Gradient Descent Algorithm for Laser Beam Cleanup

Photonics ◽  
2021 ◽  
Vol 8 (5) ◽  
pp. 165
Author(s):  
Shiqing Ma ◽  
Ping Yang ◽  
Boheng Lai ◽  
Chunxuan Su ◽  
Wang Zhao ◽  
...  
Keyword(s):  
Solid State ◽  
Adaptive Optics ◽  
Gradient Descent ◽  
Solid State Laser ◽  
Gradient Estimation ◽  
State Laser ◽  
Gradient Descent Algorithm ◽  
Stochastic Parallel Gradient Descent ◽  
High Output ◽  
Improved Algorithm

For a high-power slab solid-state laser, obtaining high output power and high output beam quality are the most important indicators. Adaptive optics systems can significantly improve beam qualities by compensating for the phase distortions of the laser beams. In this paper, we developed an improved algorithm called Adaptive Gradient Estimation Stochastic Parallel Gradient Descent (AGESPGD) algorithm for beam cleanup of a solid-state laser. A second-order gradient of the search point was introduced to modify the gradient estimation, and it was introduced with the adaptive gain coefficient method into the classical Stochastic Parallel Gradient Descent (SPGD) algorithm. The improved algorithm accelerates the search for convergence and prevents it from falling into a local extremum. Simulation and experimental results show that this method reduces the number of iterations by 40%, and the algorithm stability is also improved compared with the original SPGD method.

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