A Dimension Reduction Technique for Large-Scale Structured Sparse Optimization Problems with Application to Convex Clustering | PERPUSTAKAAN UNIVERSITAS KATOLIK PARAHYANGAN

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A Dimension Reduction Technique for Large-Scale Structured Sparse Optimization Problems with Application to Convex Clustering

Yuan, Yancheng - Nama Orang; Sun, Defeng - Nama Orang; Toh, Kim-Chuan - Nama Orang; Chang, Tsung-Hui - Nama Orang;

In this paper, we propose a novel adaptive sieving (AS) technique and an enhanced AS (EAS) technique, which are solver independent and can accelerate optimization algorithms for solving large-scale convex optimization problems with intrinsic structured sparsity. We establish the finite convergence property of the AS and EAS techniques with inexact solutions of the reduced subproblems. As an important application, we apply the AS and EAS techniques to the convex clustering model, which can accelerate the state-of-the-art algorithm Ssnal by more than 7 times and the algorithm ADMM by more than 14 times.

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Barcode		Tipe Koleksi	Nomor Panggil	Lokasi	Status
art143550	null	Artikel		Gdg9-Lt3	Tersedia namun tidak untuk dipinjamkan - No Loan

Informasi Detail

Judul Seri: SIAM JOURNAL ON OPTIMIZATION; Vol.32 No.3 July-September 2022
No. Panggil: -
Penerbit: : .,
Deskripsi Fisik: p. 2294-2318
Bahasa: English
ISBN/ISSN: -
Klasifikasi: NONE
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: CONVEX OPTIMIZATION
DIMENSION REDUCTION
STRUCTURED SPARSITY
CONVEX CLUSTERING
ADAPTIVE SIEVING
Info Detail Spesifik: https://doi.org/10.1137/21M1441080
Pernyataan Tanggungjawab: Yancheng Yuan, Tsung-Hui Chang, Defeng Sun, Kim-Chuan Toh

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