A covering method for continuous global optimisation

2021 ◽  
Vol 13 (4) ◽  
pp. 369
Author(s):  
Raouf Ziadi ◽  
Abdelatif Becherif Madani
Keyword(s):  
Global Optimisation ◽  
Covering Method

A covering method for continuous global optimisation

2021 ◽  
Vol 13 (4) ◽  
pp. 369
Author(s):  
Abdelatif Becherif Madani ◽  
Raouf Ziadi
Keyword(s):  
Global Optimisation ◽  
Covering Method

scholarly journals Machine-Learning Assisted Optimisation of Free-Parameters of a Dual-Input Power Amplifier for Wideband Applications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2831
Author(s):  
Teng Wang ◽  
Wantao Li ◽  
Roberto Quaglia ◽  
Pere L. Gilabert
Keyword(s):  
Power Amplifier ◽  
Power Efficiency ◽  
Average Power ◽  
Long Term Evolution ◽  
Input Power ◽  
Global Optimisation ◽  
Power Ratio ◽  
Term Evolution ◽  
Free Parameters

This paper presents an auto-tuning approach for dual-input power amplifiers using a combination of global optimisation search algorithms and adaptive linearisation in the optimisation of a multiple-input power amplifier. The objective is to exploit the extra degrees of freedom provided by dual-input topologies to enhance the power efficiency figures along wide signal bandwidths and high peak-to-average power ratio values, while being compliant with the linearity requirements. By using heuristic search global optimisation algorithms, such as the simulated annealing or the adaptive Lipschitz Optimisation, it is possible to find the best parameter configuration for PA biasing, signal calibration, and digital predistortion linearisation to help mitigating the inherent trade-off between linearity and power efficiency. Experimental results using a load-modulated balanced amplifier as device-under-test showed that after properly tuning the selected free-parameters it was possible to maximise the power efficiency when considering long-term evolution signals with different bandwidths. For example, a carrier aggregated a long-term evolution signal with up to 200 MHz instantaneous bandwidth and a peak-to-average power ratio greater than 10 dB, and was amplified with a mean output power around 33 dBm and 22.2% of mean power efficiency while meeting the in-band (error vector magnitude lower than 1%) and out-of-band (adjacent channel leakage ratio lower than −45 dBc) linearity requirements.

A novel method of global optimisation for wavefront shaping based on the differential evolution algorithm

2021 ◽  
Vol 481 ◽  
pp. 126541
Author(s):  
Yingzi Hua ◽  
Xiubao Sui ◽  
Shenghang Zhou ◽  
Qian Chen ◽  
Guohua Gu ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Global Optimisation ◽  
Novel Method ◽  
Wavefront Shaping ◽  
Evolution Algorithm

scholarly journals A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications

2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Csaba Balázs ◽  
◽  
Melissa van Beekveld ◽  
Sascha Caron ◽  
Barry M. Dillon ◽  
...  
Keyword(s):  
Random Sampling ◽  
Detailed Comparison ◽  
Global Optimisation ◽  
Adaptive Memory ◽  
Test Functions ◽  
Bee Colony ◽  
Covariance Matrix Adaptation ◽  
Global Fit ◽  
Adaptation Evolution ◽  
Optimisation Algorithms

Abstract Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate. We describe a number of global optimisation algorithms that are not yet widely used in particle astrophysics, benchmark them against random sampling and existing techniques, and perform a detailed comparison of their performance on a range of test functions. These include four analytic test functions of varying dimensionality, and a realistic example derived from a recent global fit of weak-scale supersymmetry. Although the best algorithm to use depends on the function being investigated, we are able to present general conclusions about the relative merits of random sampling, Differential Evolution, Particle Swarm Optimisation, the Covariance Matrix Adaptation Evolution Strategy, Bayesian Optimisation, Grey Wolf Optimisation, and the PyGMO Artificial Bee Colony, Gaussian Particle Filter and Adaptive Memory Programming for Global Optimisation algorithms.

Construction of systems of polynomial equations with exact p-Divisibility via the covering method

2014 ◽  
Vol 13 (06) ◽  
pp. 1450013 ◽  
Author(s):  
Francis N. Castro ◽  
Ivelisse M. Rubio
Keyword(s):  
Exponential Sums ◽  
Polynomial Equations ◽  
Prime Field ◽  
Elementary Method ◽  
Covering Method ◽  
Systems Of Polynomial Equations

We present an elementary method to compute the exact p-divisibility of exponential sums of systems of polynomial equations over the prime field. Our results extend results by Carlitz and provide concrete and simple conditions to construct families of polynomial equations that are solvable over the prime field.

Design of damping properties of hybrid laminates through a global optimisation strategy

2012 ◽  
Vol 94 (11) ◽  
pp. 3309-3320 ◽  
Author(s):  
Marco Montemurro ◽  
Yao Koutsawa ◽  
Salim Belouettar ◽  
Angela Vincenti ◽  
Paolo Vannucci
Keyword(s):  
Global Optimisation ◽  
Damping Properties ◽  
Hybrid Laminates

scholarly journals Global optimisation-based control algorithms applied to boundary layer transition problems

2004 ◽  
Vol 12 (4) ◽  
pp. 475-490 ◽  
Author(s):  
G.V. Veres ◽  
O.R. Tutty ◽  
E. Rogers ◽  
P.A. Nelson
Keyword(s):  
Boundary Layer ◽  
Boundary Layer Transition ◽  
Global Optimisation ◽  
Control Algorithms ◽  
Layer Transition
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