A new hybrid conjugate gradient algorithm for unconstrained optimization with inexact line search

Jardow, Fanar N.; Al-Naemi, Ghada M.

doi:10.11591/ijeecs.v20.i2.pp939-947

Cited by 7 publications

(7 citation statements)

References 22 publications

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“…According to [13,[19][20][21][22][23][24][25][26], hybrid CGMs have recently been suggested. The novel hybrid CGM, built by convex combination, satisfies the necessary descent condition, according to the author in [19].…”

Section: Review Of Existing Methodsmentioning

confidence: 99%

Exploring Enhanced Conjugate Gradient Methods: A Novel Family of Techniques for Efficient Unconstrained Minimization

Onuoha,

Ayinla,

Egenti

et al. 2024

EJMS

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Given that the conjugate gradient method (CGM) is computationally efficient and user-friendly, it is often used to address large-scale, unconstrained minimization issues. Numerous researchers have created new conjugate gradient (CG) update parameters by modifying the initial set, also referred to as classical CGMs. This has resulted in the development of several hybrid approaches. This work's major goal is to create a new family of techniques that can be used to create even more new methods. Consequently, Hestenes-Stiefel's update parameter and a new family involving Polak-Ribiere-Polyak and Liu-Storey CGMs are considered. By changing the parameters of this CGM family, a novel approach that possesses sufficient descent characteristics is obtained. A numerical experiment including many unconstrained minimization problems (UMP) is carried out to assess the novel method's efficacy compared to existing approaches. The result reveals that the new CG approach performs better than the current ones.

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Section: Review Of Existing Methodsmentioning

confidence: 99%

Exploring Enhanced Conjugate Gradient Methods: A Novel Family of Techniques for Efficient Unconstrained Minimization

Onuoha,

Ayinla,

Egenti

et al. 2024

EJMS

View full text Add to dashboard Cite

show abstract

“…In average pooling, the sliding window's result is computed using the average statistic; so, the averaging of the feature map is first done before it is transferred to the next layer. Therefore, the use of the pooling layer when building a deep neural network (DNN) can significantly reduce the amount of information conveyed by an image [31].…”

Section: Convolution Layermentioning

confidence: 99%

Aerial image semantic segmentation based on 3D fits a small dataset of 1D

Ahmed

Hazry

Hussain³

2023

IJ-AI

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<p>Time restrictions and lack of precision demand that the initial technique be abandoned. Even though the remaining datasets had fewer identified classes than initially planned for the study, the labels were more accurate. Because of the need for additional data, a single network cannot categorize all the essential elements in a picture, including bodies of water, roads, trees, buildings, and crops. However, the final network gains some invariance in detecting these classes with environmental changes due to the different geographic positions of roads and buildings discovered in the final datasets, which could be valuable in future navigation research. At the moment, binary classifications of a single class are the only datasets that can be used for the semantic segmentation of aerial images. Even though some pictures have more than one classification, images of roads and buildings were only found in a significant number of samples. Then, the building datasets were pooled to produce a larger dataset and for the constructed models to gain some invariance on image location. Because of the massive disparity in sample size, road datasets needed to be integrated.</p>

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“…In order to improve the computational efficiency of the standard CG method, a special type of conjugate gradient methods have recently been extensively investigated [17]- [20]. The approaches in [9], [18] propose a conjugacy condition of the form.…”

Section: 𝑓(𝑥mentioning

confidence: 99%

An enhanced fletcher-reeves-like conjugate gradient methods for image restoration

Hassan,

Jabbar,

Laylani

et al. 2023

IJECE

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Noise is an unavoidable aspect of modern camera technology, causing a decline in the overall visual quality of the images. Efforts are underway to diminish noise without compromising essential image features like edges, corners, and other intricate structures. Numerous techniques have already been suggested by many researchers for noise reduction, each with its unique set of benefits and drawbacks. Denoising images is a basic challenge in image processing. We describe a two-phase approach for removing impulse noise in this study. The adaptive median filter (AMF) for salt-and-pepper noise identifies noise candidates in the first phase. The second step minimizes an edge-preserving regularization function using a novel hybrid conjugate gradient approach. To generate the new improved search direction, the new algorithm takes advantage of two well-known successful conjugate gradient techniques. The descent property and global convergence are proven for the new methods. The obtained numerical results reveal that, when applied to image restoration, the new algorithms are superior to the classical fletcher reeves (FR) method in the same domain in terms of maintaining image quality and efficiency.

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A new hybrid conjugate gradient algorithm for unconstrained optimization with inexact line search

Cited by 7 publications

References 22 publications

Exploring Enhanced Conjugate Gradient Methods: A Novel Family of Techniques for Efficient Unconstrained Minimization

Exploring Enhanced Conjugate Gradient Methods: A Novel Family of Techniques for Efficient Unconstrained Minimization

Aerial image semantic segmentation based on 3D fits a small dataset of 1D

An enhanced fletcher-reeves-like conjugate gradient methods for image restoration

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