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Paper Abstract and Keywords
Presentation 2026-06-22 15:35
Trainable Linearized ADMM for Inverse Problems with PDE-based Observations
Satoshi Takabe, Shunta Arai (Science Tokyo), Tadashi Wadayama (Nitech) SIP2026-13 BioX2026-13 IE2026-13 MI2026-13
Abstract (in Japanese) (See Japanese page) 
(in English) Partial differential equations (PDEs) have often been used to directly model the measurement process in signal processing, although their numerical evaluation is costly in general. In this paper, we propose a novel alternating direction method of multipliers (ADMM)-based algorithm called physics-aware linearized ADMM (PA-LADMM) for inverse problems from PDE-based measurement processes. The key idea is the linearization of the subproblem including solving PDEs, leading to a cost-efficient update rule that calls a PDE solver and its gradient evaluation once per iteration. The algorithm has a theoretical convergence guarantee under certain conditions. In addition, we combine it with deep unfolding to train its internal parameters using supervised data. Two distinct experiments, compressed sensing with optical fiber communication and image restoration from noisy anisotropic diffusion, demonstrated the effectiveness of the proposed algorithms.
Keyword (in Japanese) (See Japanese page) 
(in English) partial differential equations / deep unfolding / compressed sensing / / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 83, SIP2026-13, pp. 63-68, June 2026.
Paper # SIP2026-13 
Date of Issue 2026-06-15 (SIP, BioX, IE, MI) 
ISSN Online edition: ISSN 2432-6380
Copyright
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reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SIP2026-13 BioX2026-13 IE2026-13 MI2026-13

Conference Information
Committee ITE-IST ITE-ME IE BioX SIP MI  
Conference Date 2026-06-22 - 2026-06-23 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIP 
Conference Code 2026-06-IST-ME-IE-BioX-SIP-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Trainable Linearized ADMM for Inverse Problems with PDE-based Observations 
Sub Title (in English)  
Keyword(1) partial differential equations  
Keyword(2) deep unfolding  
Keyword(3) compressed sensing  
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1st Author's Name Satoshi Takabe  
1st Author's Affiliation Tokyo Institute of Technology (Science Tokyo)
2nd Author's Name Shunta Arai  
2nd Author's Affiliation Tokyo Institute of Technology (Science Tokyo)
3rd Author's Name Tadashi Wadayama  
3rd Author's Affiliation Nagoya Institute of Technology (Nitech)
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Speaker Author-1 
Date Time 2026-06-22 15:35:00 
Presentation Time 25 minutes 
Registration for SIP 
Paper # SIP2026-13, BioX2026-13, IE2026-13, MI2026-13 
Volume (vol) vol.126 
Number (no) no.83(SIP), no.84(BioX), no.85(IE), no.86(MI) 
Page pp.63-68 
#Pages 6 
Date of Issue 2026-06-15 (SIP, BioX, IE, MI) 

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