A combined conjugate-gradient quasi-Newton minimization algorithm

1978 ◽  
Vol 15 (1) ◽  
pp. 200-210 ◽  
Author(s):  
A. G. Buckley
Keyword(s):  
Conjugate Gradient ◽  
Minimization Algorithm ◽  
Quasi Newton ◽  
Newton Minimization

A new three-term spectral conjugate gradient algorithm with higher numerical performance for solving large scale optimization problems based on Quasi-Newton equation

2021 ◽  
pp. 2150053
Author(s):  
Jie Guo ◽  
Zhong Wan
Keyword(s):  
Conjugate Gradient ◽  
Large Scale ◽  
Line Search ◽  
Optimization Problems ◽  
Gradient Algorithm ◽  
Large Scale Optimization ◽  
Newton Equation ◽  
Numerical Performance ◽  
Scale Optimization ◽  
Quasi Newton

A new spectral three-term conjugate gradient algorithm in virtue of the Quasi-Newton equation is developed for solving large-scale unconstrained optimization problems. It is proved that the search directions in this algorithm always satisfy a sufficiently descent condition independent of any line search. Global convergence is established for general objective functions if the strong Wolfe line search is used. Numerical experiments are employed to show its high numerical performance in solving large-scale optimization problems. Particularly, the developed algorithm is implemented to solve the 100 benchmark test problems from CUTE with different sizes from 1000 to 10,000, in comparison with some similar ones in the literature. The numerical results demonstrate that our algorithm outperforms the state-of-the-art ones in terms of less CPU time, less number of iteration or less number of function evaluation.

An Improved Method In Speech Signal Input Representation Based On DTW Technique For NN Speech Recognition System

2012 ◽  
Author(s):  
Rubita Sudirman ◽  
Sh. Hussain Salleh ◽  
Shaharuddin Salleh
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Conjugate Gradient ◽  
Template Matching ◽  
Dynamic Time Warping ◽  
Gradient Descent ◽  
Reference Frames ◽  
Time Warping ◽  
Dynamic Time ◽  
Quasi Newton

Kertas kerja ini membentangkan pemprosesan semula ciri pertuturan pemalar Pengekodan Ramalan Linear (LPC) bagi menyediakan template rujukan yang boleh diharapkan untuk set perkataan yang hendak dicam menggunakan rangkaian neural buatan. Kertas kerja ini juga mencadangkan penggunaan cirian kenyaringan yang ditakrifkan dari data pertuturan sebagai satu lagi ciri input. Algoritma Warping Masa Dinamik (DTW) menjadi asas kepada algoritma baru yang dibangunkan, ia dipanggil sebagai DTW padanan bingkai (DTW–FF). Algoritma ini direka bentuk untuk melakukan padanan bingkai bagi pemprosesan semula input LPC. Ia bertujuan untuk menyamakan bilangan bingkai input dalam set ujian dengan set rujukan. Pernormalan bingkaian ini adalah diperlukan oleh rangkaian neural yang direka untuk membanding data yang harus mempunyai kepanjangan yang sama, sedangkan perkataan yang sama dituturkan dengan kepanjangan yang berbeza–beza. Dengan melakukan padanan bingkai, bingkai input dan rujukan boleh diubahsuai supaya bilangan bingkaian sama seperti bingkaian rujukan. Satu lagi misi kertas kerja ini ialah mentakrif dan menggunakan cirian kenyaringan menggunakan algoritma penapis harmonik. Selepas kenyaringan ditakrif dan pemalar LPC dinormalkan kepada bilangan bingkaian dikehendaki, pengecaman pertuturan menggunakan rangkaian neural dilakukan. Keputusan yang baik diperoleh sehingga mencapai ketepatan setinggi 98% menggunakan kombinasi cirian DTW–FF dan cirian kenyaringan. Di akhir kertas kerja ini, perbandingan kadar convergence antara Conjugate gradient descent (CGD), Quasi–Newton, dan Steepest Gradient Descent (SGD) dilakukan untuk mendapatkan arah carian titik global yang optimal. Keputusan menunjukkan CGD memberikan nilai titik global yang paling optimal dibandingkan dengan Quasi–Newton dan SGD. Kata kunci: Warping masa dinamik, pernormalan masa, rangkaian neural, pengecaman pertuturan, conjugate gradient descent A pre–processing of linear predictive coefficient (LPC) features for preparation of reliable reference templates for the set of words to be recognized using the artificial neural network is presented in this paper. The paper also proposes the use of pitch feature derived from the recorded speech data as another input feature. The Dynamic Time Warping algorithm (DTW) is the back–bone of the newly developed algorithm called DTW fixing frame algorithm (DTW–FF) which is designed to perform template matching for the input preprocessing. The purpose of the new algorithm is to align the input frames in the test set to the template frames in the reference set. This frame normalization is required since NN is designed to compare data of the same length, however same speech varies in their length most of the time. By doing frame fixing, the input frames and the reference frames are adjusted to the same number of frames according to the reference frames. Another task of the study is to extract pitch features using the Harmonic Filter algorithm. After pitch extraction and linear predictive coefficient (LPC) features fixed to a desired number of frames, speech recognition using neural network can be performed and results showed a very promising solution. Result showed that as high as 98% recognition can be achieved using combination of two features mentioned above. At the end of the paper, a convergence comparison between conjugate gradient descent (CGD), Quasi–Newton, and steepest gradient descent (SGD) search direction is performed and results show that the CGD outperformed the Newton and SGD. Key words: Dynamic time warping, time normalization, neural network, speech recognition, conjugate gradient descent

A benchmark method for global optimization problems in structure determination from powder diffraction data

2010 ◽  
Vol 43 (3) ◽  
pp. 401-406 ◽  
Author(s):  
Kenneth Shankland ◽  
Anders J. Markvardsen ◽  
Christopher Rowlatt ◽  
Norman Shankland ◽  
William I. F. David
Keyword(s):  
Powder Diffraction ◽  
Conjugate Gradient ◽  
Diffraction Data ◽  
Structure Determination ◽  
Global Minimum ◽  
Optimization Problems ◽  
Powder Diffraction Data ◽  
X Ray ◽  
Newton Raphson ◽  
Quasi Newton

Quasi-Newton–Raphson minimization and conjugate gradient minimization have been used to solve the crystal structures of famotidine form B and capsaicin from X-ray powder diffraction data and characterize the χ2agreement surfaces. One million quasi-Newton–Raphson minimizations found the famotidine global minimum with a frequency ofca1 in 5000 and the capsaicin global minimum with a frequency ofca1 in 10 000. These results, which are corroborated by conjugate gradient minimization, demonstrate the existence of numerous pathways from some of the highest points on these χ2agreement surfaces to the respective global minima, which are passable using only downhill moves. This important observation has significant ramifications for the development of improved structure determination algorithms.

scholarly journals NEURAL NETWORKS FOR THE PREDICTION OF FRESH PROPERTIES AND COMPRESSIVE STRENGTH OF FLOWABLE CONCRETE

2019 ◽  
pp. 183-197
Author(s):  
Revathy Jayaseelan ◽  
Gajalskshmi Pandulu ◽  
Ashwini G
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Network Model ◽  
Neural Network Model ◽  
Conjugate Gradient ◽  
Back Propagation ◽  
Fresh Properties ◽  
Levenberg Marquardt ◽  
Oxide Particles ◽  
Quasi Newton

This paper presents the prediction of fresh concrete properties and compressive strength of flowable concrete through neural network approach. A comprehensive data set was generated from the experiments performed in the laboratory under standard conditions. The flowable concrete was made with two different types of micro particles and with single nano particles. The input parameter was chosen for the neural network model as cement, fine aggregate, coarse aggregate, superplasticizer, water-cement ratio, micro aluminium oxide particles, micro titanium oxide particles, and nano silica. The output parameter includes the slump Flow, L-Box flow, V Funnel flow and compressive strength of the flowable concrete. To develop a suitable neural network model, several training algorithms were used such as BFGS Quasi- Newton back propagation, Fletcher-Powell conjugate gradient back propagation, Polak - Ribiere conjugate gradient back propagation, Gradient descent with adaptive linear back propagation and Levenberg-Marquardt back propagation. It was found that BFGS Quasi- Newton back propagation and Levenberg-Marquardt back propagation algorithm provides more than 90% on the prediction accuracy. Hence, the model performance was agreeable for prediction purposes for the fresh properties and compressive strength of flowable concrete.

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