Decoding as a linear ill-posed problem: The entropy minimization approach
The problem of decoding can be thought of as consisting of solving an ill-posed, linear inverse problem with noisy data and box constraints upon the unknowns. Specificially, we aimed to solve $\bA\bx+\be=\by,$ where $\bA$ is a matrix with positive entries and $\by$ is a vector with positive entries....
- Autores:
-
Gauthier-Umaña, Valérie
Gzyl, Henryk
ter Horst, Enrique
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2025
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/76128
- Acceso en línea:
- https://hdl.handle.net/1992/76128
https://doi.org/10.3934/math.2025192
- Palabra clave:
- ill-posed inverse problems
decoding as inverse problem
convex optimization
gaussian random variables
Ingeniería
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
id |
UNIANDES2_8a871150185afb01ca759d0adaef9ebf |
---|---|
oai_identifier_str |
oai:repositorio.uniandes.edu.co:1992/76128 |
network_acronym_str |
UNIANDES2 |
network_name_str |
Séneca: repositorio Uniandes |
repository_id_str |
|
dc.title.none.fl_str_mv |
Decoding as a linear ill-posed problem: The entropy minimization approach |
title |
Decoding as a linear ill-posed problem: The entropy minimization approach |
spellingShingle |
Decoding as a linear ill-posed problem: The entropy minimization approach ill-posed inverse problems decoding as inverse problem convex optimization gaussian random variables Ingeniería |
title_short |
Decoding as a linear ill-posed problem: The entropy minimization approach |
title_full |
Decoding as a linear ill-posed problem: The entropy minimization approach |
title_fullStr |
Decoding as a linear ill-posed problem: The entropy minimization approach |
title_full_unstemmed |
Decoding as a linear ill-posed problem: The entropy minimization approach |
title_sort |
Decoding as a linear ill-posed problem: The entropy minimization approach |
dc.creator.fl_str_mv |
Gauthier-Umaña, Valérie Gzyl, Henryk ter Horst, Enrique |
dc.contributor.author.none.fl_str_mv |
Gauthier-Umaña, Valérie Gzyl, Henryk ter Horst, Enrique |
dc.contributor.researchgroup.none.fl_str_mv |
Facultad de Ingeniería::TICSw: Tecnologías de Información y Construcción de Software |
dc.subject.keyword.none.fl_str_mv |
ill-posed inverse problems decoding as inverse problem convex optimization gaussian random variables |
topic |
ill-posed inverse problems decoding as inverse problem convex optimization gaussian random variables Ingeniería |
dc.subject.themes.none.fl_str_mv |
Ingeniería |
description |
The problem of decoding can be thought of as consisting of solving an ill-posed, linear inverse problem with noisy data and box constraints upon the unknowns. Specificially, we aimed to solve $\bA\bx+\be=\by,$ where $\bA$ is a matrix with positive entries and $\by$ is a vector with positive entries. It is required that $\bx\in\cK$, which is specified below, and we considered two points of view about the noise term, both of which were implied as unknowns to be determined. On the one hand, the error can be thought of as a confounding error, intentionally added to the coded message. On the other hand, we may think of the error as a true additive transmission-measurement error. We solved the problem by minimizing an entropy of the Fermi-Dirac type defined on the set of all constraints of the problem. Our approach provided a consistent way to recover the message and the noise from the measurements. In an example with a generator code matrix of the Reed-Solomon type, we examined the two points of view about the noise. As our approach enabled us to recursively decrease the $\ell_1$ norm of the noise as part of the solution procedure, we saw that, if the required norm of the noise was too small, the message was not well recovered. Our work falls within the general class of near-optimal signal recovery line of work. We also studied the case with Gaussian random matrices. |
publishDate |
2025 |
dc.date.accessioned.none.fl_str_mv |
2025-03-20T12:54:55Z |
dc.date.available.none.fl_str_mv |
2025-03-20T12:54:55Z |
dc.date.issued.none.fl_str_mv |
2025-02-27 |
dc.type.none.fl_str_mv |
Artículo de revista |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.type.content.none.fl_str_mv |
Text |
dc.type.redcol.none.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
format |
http://purl.org/coar/resource_type/c_6501 |
dc.identifier.issn.none.fl_str_mv |
2473-6988 |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/1992/76128 |
dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.3934/math.2025192 |
dc.identifier.instname.none.fl_str_mv |
instname:Universidad de los Andes |
dc.identifier.reponame.none.fl_str_mv |
reponame:Repositorio Institucional Séneca |
dc.identifier.repourl.none.fl_str_mv |
repourl:https://repositorio.uniandes.edu.co/ |
identifier_str_mv |
2473-6988 instname:Universidad de los Andes reponame:Repositorio Institucional Séneca repourl:https://repositorio.uniandes.edu.co/ |
url |
https://hdl.handle.net/1992/76128 https://doi.org/10.3934/math.2025192 |
dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.relation.citationendpage.none.fl_str_mv |
4152 |
dc.relation.citationissue.none.fl_str_mv |
4 |
dc.relation.citationstartpage.none.fl_str_mv |
4139 |
dc.relation.citationvolume.none.fl_str_mv |
10 |
dc.relation.ispartofjournal.none.fl_str_mv |
AIMS Mathematics |
dc.relation.references.none.fl_str_mv |
1. F. L. Bauer, Decrypted secrets: Methods and maxims on cryptography, Berlin: Springer-Verlag, 1997. 2. J. M. Borwein, A. S. Lewis, Convex analysis and nonlinear optimization, 2nd Edition, Berlin: CMS-Springer, 2006. 3. D. Burshtein, I. Goldenberg, Improved linear programming decoding and bounds on the minimum distance of LDPC codes, IEEE Inf. Theory Work., 2010. Available from: https://ieeexplore. ieee.org/document/5592887. 4. E. Candes, T. Tao, Decoding by linear programming, IEEE Tran. Inf. Theory, 51 (2005), 4203– 4215. http://dx.doi.org/10.1109/TIT.2005.858979 5. E. Candes, T. Tao, Near optimal signal recovery from random projections: Universal encoding strategies, IEEE Tran. Inf. Theory, 52 (2006), 5406–5425. http://dx.doi.org/10.1109/TIT.2006.885507 6. C. Daskalakis, G. Alexandros, A. G. Dimakis, R. M. Karp, M. J. Wainwright, Probabilistic analysis of linear programming decoding, IEEE Tran. Inf. Theory, 54 (2008), 3565–3578. http://dx.doi.org/10.1109/TIT.2008.926452 7. S. El Rouayyheb, C. N. Georghiades, Graph theoretic methods in coding theory, Classical, Semiclass. Quant. Noise, 2012, 53–62. https://doi.org/10.1007/978-1-4419-6624-7 5 8. J. Feldman, M. J. Wainwright, D. R. Karger, Using linear programming to decode binary linear codes, IEEE Tran. Inf. Theory, 51 (2005), 954–972. https://doi.org/10.1109/TIT.2004.842696 9. F. Gamboa, H. Gzyl, Linear programming with maximum entropy, Math. Comput. Modeling, 13 (1990), 49–52. 10. Y. S. Han, A new treatment of priority-first search maximum-likelihood soft-decision decoding of linear block codes, IEEE Tran. Inf. Theory, 44 (1998), 3091–3096. https://doi.org/10.1109/18.737538 11. M. Helmiling, Advances in mathematical programming-based error-correction decoding, OPUS Koblen., 2015. Available from: https://kola.opus.hbz-nrw.de/frontdoor/index/ index/year/2015/docId/948. 12. M. Helmling, S. Ruzika, A. Tanatmis, Mathematical programming decoding of binary linear codes: Theory and algorithms, IEEE Tran. Inf. Theory, 58 (2012), 4753–4769. https://doi.org/10.1109/TIT.2012.2191697 13. M. R. Islam, Linear programming decoding: The ultimate decoding technique for low density parity check codes, Radioel. Commun. Syst., 56 (2013), 57–72. https://doi.org/10.3103/S0735272713020015 14. T. Kaneko, T. Nishijima, S. Hirasawa, An improvement of soft-decision maximum-likelihood decoding algorithm using hard-decision bounded-distance decoding, IEEE Tran. Inf. Theory, 43 (1997), 1314–1319. https://doi.org/10.1109/18.605601 15. S. B. McGrayne, The theory that would not die. How Bayes’ rule cracked the enigma code, hunted down Russian submarines, & emerged triumphant from two centuries of controversy, New Haven: Yale University Press, 2011. 16. R. J. McEliece, A public-key cryptosystem based on algebraic, Coding Th., 4244 (1978), 114–116. 17. H. Mohammad, N. Taghavi, P. H. Siegel, Adaptive methods for linear programming decoding, IEEE Tran. Inf. Theory, 54 (2008), 5396–5410. https://doi.org/10.1109/TIT.2008.2006384 18. G. Xie, F. Fu, H. Li, W. Du, Y. Zhong, L. Wang, et al, A gradient-enhanced physicsinformed neural networks method for the wave equation, Eng. Anal. Bound. Ele., 166 (2024). https://doi.org/10.1016/j.enganabound.2024.105802 19. Q. Yin, X. B. Shu, Y. Guo, Z. Y. Wang, Optimal control of stochastic differential equations with random impulses and the Hamilton-Jacobi-Bellman equation, Optimal Control Appl. Methods, 45 (2024), 2113–2135. https://doi.org/10.1002/oca.3139 20. B. Zolfaghani, K. Bibak, T. Koshiba, The odyssey of entropy: Cryptography, Entropy, 24 (2022), 266–292. https://doi.org/10.3390/e24020266 |
dc.rights.accessrights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.coar.none.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.format.extent.none.fl_str_mv |
14 páginas |
dc.format.mimetype.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidad de los Andes |
dc.publisher.faculty.none.fl_str_mv |
Facultad de Ingeniería |
dc.publisher.department.none.fl_str_mv |
Departamento de Ingeniería de Sistemas |
publisher.none.fl_str_mv |
Universidad de los Andes |
institution |
Universidad de los Andes |
bitstream.url.fl_str_mv |
https://repositorio.uniandes.edu.co/bitstreams/0cdc949a-98c1-4787-8eb1-ce59d3b866cf/download https://repositorio.uniandes.edu.co/bitstreams/5cc74d01-08cb-4d4d-83e5-d5f238b2c1d9/download https://repositorio.uniandes.edu.co/bitstreams/ecd8f5da-8b47-4135-b371-4977100d0ff8/download https://repositorio.uniandes.edu.co/bitstreams/08da0415-8110-44ae-b547-4d55e56e9b24/download |
bitstream.checksum.fl_str_mv |
c3368784454cb8b77d242815bbaba1bb ae9e573a68e7f92501b6913cc846c39f ae1329de38a20ca40fd67d1489371ae8 ffe24214940ce6a1dc560746250677d3 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositorio institucional Séneca |
repository.mail.fl_str_mv |
adminrepositorio@uniandes.edu.co |
_version_ |
1831927835446476800 |
spelling |
Al consultar y hacer uso de este recurso, está aceptando las condiciones de uso establecidas por los autoresinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Gauthier-Umaña, ValérieGzyl, Henrykter Horst, EnriqueFacultad de Ingeniería::TICSw: Tecnologías de Información y Construcción de Software2025-03-20T12:54:55Z2025-03-20T12:54:55Z2025-02-272473-6988https://hdl.handle.net/1992/76128https://doi.org/10.3934/math.2025192instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/The problem of decoding can be thought of as consisting of solving an ill-posed, linear inverse problem with noisy data and box constraints upon the unknowns. Specificially, we aimed to solve $\bA\bx+\be=\by,$ where $\bA$ is a matrix with positive entries and $\by$ is a vector with positive entries. It is required that $\bx\in\cK$, which is specified below, and we considered two points of view about the noise term, both of which were implied as unknowns to be determined. On the one hand, the error can be thought of as a confounding error, intentionally added to the coded message. On the other hand, we may think of the error as a true additive transmission-measurement error. We solved the problem by minimizing an entropy of the Fermi-Dirac type defined on the set of all constraints of the problem. Our approach provided a consistent way to recover the message and the noise from the measurements. In an example with a generator code matrix of the Reed-Solomon type, we examined the two points of view about the noise. As our approach enabled us to recursively decrease the $\ell_1$ norm of the noise as part of the solution procedure, we saw that, if the required norm of the noise was too small, the message was not well recovered. Our work falls within the general class of near-optimal signal recovery line of work. We also studied the case with Gaussian random matrices.14 páginasapplication/pdfengUniversidad de los AndesFacultad de IngenieríaDepartamento de Ingeniería de SistemasDecoding as a linear ill-posed problem: The entropy minimization approachArtículo de revistainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTill-posed inverse problemsdecoding as inverse problemconvex optimizationgaussian random variablesIngeniería41524413910AIMS Mathematics1. F. L. Bauer, Decrypted secrets: Methods and maxims on cryptography, Berlin: Springer-Verlag, 1997. 2. J. M. Borwein, A. S. Lewis, Convex analysis and nonlinear optimization, 2nd Edition, Berlin: CMS-Springer, 2006. 3. D. Burshtein, I. Goldenberg, Improved linear programming decoding and bounds on the minimum distance of LDPC codes, IEEE Inf. Theory Work., 2010. Available from: https://ieeexplore. ieee.org/document/5592887. 4. E. Candes, T. Tao, Decoding by linear programming, IEEE Tran. Inf. Theory, 51 (2005), 4203– 4215. http://dx.doi.org/10.1109/TIT.2005.858979 5. E. Candes, T. Tao, Near optimal signal recovery from random projections: Universal encoding strategies, IEEE Tran. Inf. Theory, 52 (2006), 5406–5425. http://dx.doi.org/10.1109/TIT.2006.885507 6. C. Daskalakis, G. Alexandros, A. G. Dimakis, R. M. Karp, M. J. Wainwright, Probabilistic analysis of linear programming decoding, IEEE Tran. Inf. Theory, 54 (2008), 3565–3578. http://dx.doi.org/10.1109/TIT.2008.926452 7. S. El Rouayyheb, C. N. Georghiades, Graph theoretic methods in coding theory, Classical, Semiclass. Quant. Noise, 2012, 53–62. https://doi.org/10.1007/978-1-4419-6624-7 5 8. J. Feldman, M. J. Wainwright, D. R. Karger, Using linear programming to decode binary linear codes, IEEE Tran. Inf. Theory, 51 (2005), 954–972. https://doi.org/10.1109/TIT.2004.842696 9. F. Gamboa, H. Gzyl, Linear programming with maximum entropy, Math. Comput. Modeling, 13 (1990), 49–52. 10. Y. S. Han, A new treatment of priority-first search maximum-likelihood soft-decision decoding of linear block codes, IEEE Tran. Inf. Theory, 44 (1998), 3091–3096. https://doi.org/10.1109/18.737538 11. M. Helmiling, Advances in mathematical programming-based error-correction decoding, OPUS Koblen., 2015. Available from: https://kola.opus.hbz-nrw.de/frontdoor/index/ index/year/2015/docId/948. 12. M. Helmling, S. Ruzika, A. Tanatmis, Mathematical programming decoding of binary linear codes: Theory and algorithms, IEEE Tran. Inf. Theory, 58 (2012), 4753–4769. https://doi.org/10.1109/TIT.2012.2191697 13. M. R. Islam, Linear programming decoding: The ultimate decoding technique for low density parity check codes, Radioel. Commun. Syst., 56 (2013), 57–72. https://doi.org/10.3103/S0735272713020015 14. T. Kaneko, T. Nishijima, S. Hirasawa, An improvement of soft-decision maximum-likelihood decoding algorithm using hard-decision bounded-distance decoding, IEEE Tran. Inf. Theory, 43 (1997), 1314–1319. https://doi.org/10.1109/18.605601 15. S. B. McGrayne, The theory that would not die. How Bayes’ rule cracked the enigma code, hunted down Russian submarines, & emerged triumphant from two centuries of controversy, New Haven: Yale University Press, 2011. 16. R. J. McEliece, A public-key cryptosystem based on algebraic, Coding Th., 4244 (1978), 114–116. 17. H. Mohammad, N. Taghavi, P. H. Siegel, Adaptive methods for linear programming decoding, IEEE Tran. Inf. Theory, 54 (2008), 5396–5410. https://doi.org/10.1109/TIT.2008.2006384 18. G. Xie, F. Fu, H. Li, W. Du, Y. Zhong, L. Wang, et al, A gradient-enhanced physicsinformed neural networks method for the wave equation, Eng. Anal. Bound. Ele., 166 (2024). https://doi.org/10.1016/j.enganabound.2024.105802 19. Q. Yin, X. B. Shu, Y. Guo, Z. Y. Wang, Optimal control of stochastic differential equations with random impulses and the Hamilton-Jacobi-Bellman equation, Optimal Control Appl. Methods, 45 (2024), 2113–2135. https://doi.org/10.1002/oca.3139 20. B. Zolfaghani, K. Bibak, T. Koshiba, The odyssey of entropy: Cryptography, Entropy, 24 (2022), 266–292. https://doi.org/10.3390/e24020266ORIGINAL10.3934_math.2025192 (1).pdf10.3934_math.2025192 (1).pdfapplication/pdf253804https://repositorio.uniandes.edu.co/bitstreams/0cdc949a-98c1-4787-8eb1-ce59d3b866cf/downloadc3368784454cb8b77d242815bbaba1bbMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-82535https://repositorio.uniandes.edu.co/bitstreams/5cc74d01-08cb-4d4d-83e5-d5f238b2c1d9/downloadae9e573a68e7f92501b6913cc846c39fMD52TEXT10.3934_math.2025192 (1).pdf.txt10.3934_math.2025192 (1).pdf.txtExtracted texttext/plain39084https://repositorio.uniandes.edu.co/bitstreams/ecd8f5da-8b47-4135-b371-4977100d0ff8/downloadae1329de38a20ca40fd67d1489371ae8MD53THUMBNAIL10.3934_math.2025192 (1).pdf.jpg10.3934_math.2025192 (1).pdf.jpgGenerated Thumbnailimage/jpeg14757https://repositorio.uniandes.edu.co/bitstreams/08da0415-8110-44ae-b547-4d55e56e9b24/downloadffe24214940ce6a1dc560746250677d3MD541992/76128oai:repositorio.uniandes.edu.co:1992/761282025-03-28 09:19:09.356open.accesshttps://repositorio.uniandes.edu.coRepositorio institucional Sénecaadminrepositorio@uniandes.edu.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 |