Geometrical interpretation and improvements of the Blahut-Arimoto's algorithm
The paper first recalls the Blahut Arimoto algorithm for computing the capacity of arbitrary discrete memoryless channels, as an example of an iterative algorithm working with probability density estimates. Then, a geometrical interpretation of this algorithm based on projections onto linear and exp...
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| creator | Naja, Z. Alberge, F. Duhamel, P. |
| description | The paper first recalls the Blahut Arimoto algorithm for computing the capacity of arbitrary discrete memoryless channels, as an example of an iterative algorithm working with probability density estimates. Then, a geometrical interpretation of this algorithm based on projections onto linear and exponential families of probabilities is provided. Finally, this understanding allows also to propose to write the Blahut-Arimoto algorithm, as a true proximal point algorithm. it is shown that the corresponding version has an improved convergence rate, compared to the initial algorithm, as well as in comparison with other improved versions. |
| doi_str_mv | 10.1109/ICASSP.2009.4960131 |
| format | Conference Proceeding |
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Then, a geometrical interpretation of this algorithm based on projections onto linear and exponential families of probabilities is provided. Finally, this understanding allows also to propose to write the Blahut-Arimoto algorithm, as a true proximal point algorithm. it is shown that the corresponding version has an improved convergence rate, compared to the initial algorithm, as well as in comparison with other improved versions.</description><subject>Approximation algorithms</subject><subject>Blahut-Arimoto algorithm</subject><subject>Capacity planning</subject><subject>Channel capacity</subject><subject>Computer Science</subject><subject>Convergence</subject><subject>Convergence speed</subject><subject>Engineering Sciences</subject><subject>Entropy</subject><subject>Geometrical interpretation</subject><subject>Information Theory</subject><subject>Iterative algorithm</subject><subject>Iterative algorithms</subject><subject>Mathematics</subject><subject>Memoryless systems</subject><subject>Mutual information</subject><subject>Probability distribution</subject><subject>Proximal point method</subject><subject>Random variables</subject><subject>Signal and Image processing</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9781424423538</isbn><isbn>1424423538</isbn><isbn>9781424423545</isbn><isbn>1424423546</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkE9Lw0AQxdd_YKn9BL3kJh5Sd3Znk-yxFm2FikIVvIXZZmNWkmxJ1oLf3kiL4LsMzPsxzHuMTYHPALi-fVzMN5uXmeBcz1AnHCScsIlOM0CBKKRCdcpGQqY6Bs3fz_55MjtnI1CCxwmgvmSTvv_kg1BJQDViT0vrGxs6t6U6cm2w3a6zgYLzbURtEblm1_m9bWwb-siXUahsdFdT9RXieecaH_x1H1H94TsXquaKXZRU93ZynGP29nD_uljF6-flEGMdV0JhiLcGkqzgQJk1qLThslBGJBKNIkyJrKGERElJhigLg0lqNS-oNJxkAWUqx-zmcLeiOt8Nf1D3nXty-Wq-zn93Q0BUKeAeBnZ6YJ219g8-Fil_AAxaYz4</recordid><startdate>200904</startdate><enddate>200904</enddate><creator>Naja, Z.</creator><creator>Alberge, F.</creator><creator>Duhamel, P.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-7942-0934</orcidid><orcidid>https://orcid.org/0000-0001-7709-9684</orcidid></search><sort><creationdate>200904</creationdate><title>Geometrical interpretation and improvements of the Blahut-Arimoto's algorithm</title><author>Naja, Z. ; Alberge, F. ; Duhamel, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-h254t-cb168d01a8eb459b03d5b2634b5a47aaeba6a2fa68443db467e90dafb0a3d1f73</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Approximation algorithms</topic><topic>Blahut-Arimoto algorithm</topic><topic>Capacity planning</topic><topic>Channel capacity</topic><topic>Computer Science</topic><topic>Convergence</topic><topic>Convergence speed</topic><topic>Engineering Sciences</topic><topic>Entropy</topic><topic>Geometrical interpretation</topic><topic>Information Theory</topic><topic>Iterative algorithm</topic><topic>Iterative algorithms</topic><topic>Mathematics</topic><topic>Memoryless systems</topic><topic>Mutual information</topic><topic>Probability distribution</topic><topic>Proximal point method</topic><topic>Random variables</topic><topic>Signal and Image processing</topic><toplevel>online_resources</toplevel><creatorcontrib>Naja, Z.</creatorcontrib><creatorcontrib>Alberge, F.</creatorcontrib><creatorcontrib>Duhamel, P.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Naja, Z.</au><au>Alberge, F.</au><au>Duhamel, P.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Geometrical interpretation and improvements of the Blahut-Arimoto's algorithm</atitle><btitle>2009 IEEE International Conference on Acoustics, Speech and Signal Processing</btitle><stitle>ICASSP</stitle><date>2009-04</date><risdate>2009</risdate><spage>2505</spage><epage>2508</epage><pages>2505-2508</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9781424423538</isbn><isbn>1424423538</isbn><eisbn>9781424423545</eisbn><eisbn>1424423546</eisbn><abstract>The paper first recalls the Blahut Arimoto algorithm for computing the capacity of arbitrary discrete memoryless channels, as an example of an iterative algorithm working with probability density estimates. Then, a geometrical interpretation of this algorithm based on projections onto linear and exponential families of probabilities is provided. Finally, this understanding allows also to propose to write the Blahut-Arimoto algorithm, as a true proximal point algorithm. it is shown that the corresponding version has an improved convergence rate, compared to the initial algorithm, as well as in comparison with other improved versions.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2009.4960131</doi><tpages>4</tpages><orcidid>https://orcid.org/0000-0002-7942-0934</orcidid><orcidid>https://orcid.org/0000-0001-7709-9684</orcidid><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 1520-6149 |
| ispartof | 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009, p.2505-2508 |
| issn | 1520-6149 2379-190X |
| language | eng |
| recordid | cdi_ieee_primary_4960131 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Approximation algorithms Blahut-Arimoto algorithm Capacity planning Channel capacity Computer Science Convergence Convergence speed Engineering Sciences Entropy Geometrical interpretation Information Theory Iterative algorithm Iterative algorithms Mathematics Memoryless systems Mutual information Probability distribution Proximal point method Random variables Signal and Image processing |
| title | Geometrical interpretation and improvements of the Blahut-Arimoto's algorithm |
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