@InProceedings{7362673,
  author = {A. Onose and B. Dumitrescu},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Adaptive cyclic and randomized coordinate descent for the sparse total least squares problem},
  year = {2015},
  pages = {1696-1700},
  abstract = {Coordinate descent (CD) is a simple and general optimization technique. We use it to solve the sparse total least squares problem in an adaptive manner, working on the l1-regularized Rayleigh quotient function. We propose two algorithmic approaches for choosing the coordinates: cyclic and randomized. In both cases, the number of CD steps per time instant is a parameter that can serve as a trade-off between complexity and performance. We present numerical experiments showing that the proposed algorithms can approach stationary error near that of the oracle. The randomized algorithm is slightly better than the cyclic one with respect to convergence speed.},
  keywords = {computational complexity;filtering theory;least squares approximations;optimisation;randomised algorithms;adaptive cyclic coordinate descent;randomized coordinate descent;sparse total least squares problem;optimization technique;randomized algorithm;sparse filter;l1-regularized Rayleigh quotient function;Signal processing algorithms;Complexity theory;Europe;Adaptive algorithms;Finite impulse response filters;Indexes;adaptive algorithm;channel identification;sparse filter;total least squares;coordinate descent;randomization},
  doi = {10.1109/EUSIPCO.2015.7362673},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570102963.pdf},
}

{"_id":"eQyyKw3RRD33onhJN","bibbaseid":"onose-dumitrescu-adaptivecyclicandrandomizedcoordinatedescentforthesparsetotalleastsquaresproblem-2015","authorIDs":[],"author_short":["Onose, A.","Dumitrescu, B."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["A."],"propositions":[],"lastnames":["Onose"],"suffixes":[]},{"firstnames":["B."],"propositions":[],"lastnames":["Dumitrescu"],"suffixes":[]}],"booktitle":"2015 23rd European Signal Processing Conference (EUSIPCO)","title":"Adaptive cyclic and randomized coordinate descent for the sparse total least squares problem","year":"2015","pages":"1696-1700","abstract":"Coordinate descent (CD) is a simple and general optimization technique. We use it to solve the sparse total least squares problem in an adaptive manner, working on the l1-regularized Rayleigh quotient function. We propose two algorithmic approaches for choosing the coordinates: cyclic and randomized. In both cases, the number of CD steps per time instant is a parameter that can serve as a trade-off between complexity and performance. We present numerical experiments showing that the proposed algorithms can approach stationary error near that of the oracle. The randomized algorithm is slightly better than the cyclic one with respect to convergence speed.","keywords":"computational complexity;filtering theory;least squares approximations;optimisation;randomised algorithms;adaptive cyclic coordinate descent;randomized coordinate descent;sparse total least squares problem;optimization technique;randomized algorithm;sparse filter;l1-regularized Rayleigh quotient function;Signal processing algorithms;Complexity theory;Europe;Adaptive algorithms;Finite impulse response filters;Indexes;adaptive algorithm;channel identification;sparse filter;total least squares;coordinate descent;randomization","doi":"10.1109/EUSIPCO.2015.7362673","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570102963.pdf","bibtex":"@InProceedings{7362673,\n author = {A. Onose and B. Dumitrescu},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {Adaptive cyclic and randomized coordinate descent for the sparse total least squares problem},\n year = {2015},\n pages = {1696-1700},\n abstract = {Coordinate descent (CD) is a simple and general optimization technique. We use it to solve the sparse total least squares problem in an adaptive manner, working on the l1-regularized Rayleigh quotient function. We propose two algorithmic approaches for choosing the coordinates: cyclic and randomized. In both cases, the number of CD steps per time instant is a parameter that can serve as a trade-off between complexity and performance. We present numerical experiments showing that the proposed algorithms can approach stationary error near that of the oracle. The randomized algorithm is slightly better than the cyclic one with respect to convergence speed.},\n keywords = {computational complexity;filtering theory;least squares approximations;optimisation;randomised algorithms;adaptive cyclic coordinate descent;randomized coordinate descent;sparse total least squares problem;optimization technique;randomized algorithm;sparse filter;l1-regularized Rayleigh quotient function;Signal processing algorithms;Complexity theory;Europe;Adaptive algorithms;Finite impulse response filters;Indexes;adaptive algorithm;channel identification;sparse filter;total least squares;coordinate descent;randomization},\n doi = {10.1109/EUSIPCO.2015.7362673},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570102963.pdf},\n}\n\n","author_short":["Onose, A.","Dumitrescu, B."],"key":"7362673","id":"7362673","bibbaseid":"onose-dumitrescu-adaptivecyclicandrandomizedcoordinatedescentforthesparsetotalleastsquaresproblem-2015","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570102963.pdf"},"keyword":["computational complexity;filtering theory;least squares approximations;optimisation;randomised algorithms;adaptive cyclic coordinate descent;randomized coordinate descent;sparse total least squares problem;optimization technique;randomized algorithm;sparse filter;l1-regularized Rayleigh quotient function;Signal processing algorithms;Complexity theory;Europe;Adaptive algorithms;Finite impulse response filters;Indexes;adaptive algorithm;channel identification;sparse filter;total least squares;coordinate descent;randomization"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2015url.bib","creationDate":"2021-02-13T17:31:52.469Z","downloads":0,"keywords":["computational complexity;filtering theory;least squares approximations;optimisation;randomised algorithms;adaptive cyclic coordinate descent;randomized coordinate descent;sparse total least squares problem;optimization technique;randomized algorithm;sparse filter;l1-regularized rayleigh quotient function;signal processing algorithms;complexity theory;europe;adaptive algorithms;finite impulse response filters;indexes;adaptive algorithm;channel identification;sparse filter;total least squares;coordinate descent;randomization"],"search_terms":["adaptive","cyclic","randomized","coordinate","descent","sparse","total","squares","problem","onose","dumitrescu"],"title":"Adaptive cyclic and randomized coordinate descent for the sparse total least squares problem","year":2015,"dataSources":["eov4vbT6mnAiTpKji","knrZsDjSNHWtA9WNT"]}