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Algoritma Arnoldi untuk Penyelesaian Masalah Nilai Eigen Matriks Raksasa

Mathematical Sciences and Applications Journal · Vol. 4 No. 2 (2024) · hlm. 92–100

DOI
10.22437/msa.v4i2.36182
ISSN
2988-6481
Terbit
2024-04-30
Penerbit
Department of Mathematics, Faculty of Science and Technology Universitas Jambi
Bahasa
en

Abstrak

The computation of eigenvalues for large-scale matrices is a crucial task in various scientific and engineering domains. This research focuses on the performance of numerical techniques, particularly those utilizing Hessenberg matrices, in solving eigenvalue problems. We investigate the efficacy of these methods and their implementation on modern computational platforms. Our study reveals that increasing the size of the Hessenberg matrix significantly enhances the accuracy of eigenvalue approximations. Through extensive simulations and performance evaluations, we demonstrate that larger Hessenberg matrices provide more precise eigenvalue solutions, underscoring the importance of matrix dimension in the computational process. These findings offer valuable insights for optimizing eigenvalue computations in large-scale applications.

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  • Universitas Jambi
  • Politeknik APP Jakarta
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Rekan penulisOrganisasiArtikel bersama
Samsidar SamsidarUniversitas Jambi2
Ichy Lucya RestaUniversitas Jambi2
Putri Nurul Kusuma WhardaniAfiliasi tidak diketahui2
Maulina TanjungUniversitas Jambi2
Mhd RahmanUniversitas Jambi2
Lestari SimamoraUniversitas Jambi2
Rifa FadhilaUniversitas Jambi1
Niken RarasatiUniversitas Jambi1
Syamsyida RoziUniversitas Jambi1
Gusmanely.Z Gusmanely.ZAfiliasi tidak diketahui1
Gusmi KholijahUniversitas Jambi1
Anisa RahmawatiAfiliasi tidak diketahui1
Bayun MatsaanyAfiliasi tidak diketahui1
Anisa RahmawatiPoliteknik APP Jakarta1
Bayun MatsaanyAfiliasi tidak diketahui1
Feri Tiona PasaribuUniversitas Jambi1
Azzikra FebriantiUniversitas Jambi1
Rosi Widia AsianiUniversitas Jambi1
Arnild Augina MekarisceUniversitas Jambi1
Zuli RodhiyahUniversitas Jambi1
Samsidar SamsidarAfiliasi tidak diketahui1
Oka Lesmana SAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://online-journal.unja.ac.id/index.php/msa/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Algoritma Arnoldi untuk Penyelesaian Masalah Nilai Eigen Matriks Raksasa</dc:title>
	<dc:creator xml:lang="en">Alim, Khairul</dc:creator>
	<dc:creator xml:lang="en">Pratiwi, Nurul</dc:creator>
	<dc:creator xml:lang="en">Multahadah , Cut </dc:creator>
	<dc:creator xml:lang="en">Sormin , Corry </dc:creator>
	<dc:creator xml:lang="en">Putra, Fernando Mersa </dc:creator>
	<dc:subject xml:lang="en">Large-scale matrice</dc:subject>
	<dc:subject xml:lang="en">Arnoldi algorithm</dc:subject>
	<dc:subject xml:lang="en">Hessenberg</dc:subject>
	<dc:subject xml:lang="en">Eigenvalue computation</dc:subject>
	<dc:subject xml:lang="en">Numerical method</dc:subject>
	<dc:description xml:lang="en">The computation of eigenvalues for large-scale matrices is a crucial task in various scientific and engineering domains. This research focuses on the performance of numerical techniques, particularly those utilizing Hessenberg matrices, in solving eigenvalue problems. We investigate the efficacy of these methods and their implementation on modern computational platforms. Our study reveals that increasing the size of the Hessenberg matrix significantly enhances the accuracy of eigenvalue approximations. Through extensive simulations and performance evaluations, we demonstrate that larger Hessenberg matrices provide more precise eigenvalue solutions, underscoring the importance of matrix dimension in the computational process. These findings offer valuable insights for optimizing eigenvalue computations in large-scale applications.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Faculty of Science and Technology Universitas Jambi</dc:publisher>
	<dc:date>2024-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://online-journal.unja.ac.id/msa/article/view/36182</dc:identifier>
	<dc:identifier>10.22437/msa.v4i2.36182</dc:identifier>
	<dc:source xml:lang="en">Mathematical Sciences and Applications Journal; Vol. 4 No. 2 (2024): Mathematical Sciences and Applications Journal; 92-100</dc:source>
	<dc:source>2988-6481</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://online-journal.unja.ac.id/msa/article/view/36182/18758</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Mathematical Sciences and Applications Journal</dc:rights>
</oai_dc:dc>