Relevant bibliographies by topics / Least squares algorithm

Academic literature on the topic 'Least squares algorithm'

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Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Least squares algorithm"

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Yoo, Chang Kyoo, Su Whan Sung, and In-Beum Lee. "Generalized damped least squares algorithm." Computers & Chemical Engineering 27, no. 3 (March 2003): 423–31. http://dx.doi.org/10.1016/s0098-1354(02)00219-3.

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Zhang, Xu-Yao, Lingfeng Wang, Shiming Xiang, and Cheng-Lin Liu. "Retargeted Least Squares Regression Algorithm." IEEE Transactions on Neural Networks and Learning Systems 26, no. 9 (September 2015): 2206–13. http://dx.doi.org/10.1109/tnnls.2014.2371492.

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Peng Guo, 彭国, 李伟明 Li Weiming, 黄扬 Huang Yang, 陈艺海 Cheng Yihai, and 高兴宇 Gao Xingyu. "Improved Least Squares Unwrapping Algorithm." Laser & Optoelectronics Progress 57, no. 18 (2020): 181101. http://dx.doi.org/10.3788/lop57.181101.

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Da-Zheng Feng, Zheng Bao, and Li-Cheng Jiao. "Total least mean squares algorithm." IEEE Transactions on Signal Processing 46, no. 8 (1998): 2122–30. http://dx.doi.org/10.1109/78.705421.

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Van Huffel, Sabine. "Partial total least squares algorithm." Journal of Computational and Applied Mathematics 33, no. 1 (December 1990): 113–21. http://dx.doi.org/10.1016/0377-0427(90)90261-w.

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Javed, Shazia, and Noor Atinah Ahmad. "A Stochastic Total Least Squares Solution of Adaptive Filtering Problem." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/625280.

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An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution of adaptive TLS problem by minimizing instantaneous value of weighted cost function. Convergence analysis of the algorithm is given to show the global convergence of the proposed algorithm, provided that the stepsize parameter is appropriately chosen. The TLMS algorithm is computationally simpler than the other TLS algorithms and demonstrates a better performance as compared with the least mean square (LMS) and normalized least mean square (NLMS) algorithms. It provides minimum mean square deviation by exhibiting better convergence in misalignment for unknown system identification under noisy inputs.
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Heller, René, Michael Hippke, and Kai Rodenbeck. "Transit least-squares survey." Astronomy & Astrophysics 627 (July 2019): A66. http://dx.doi.org/10.1051/0004-6361/201935600.

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The extended Kepler mission (K2) has revealed more than 500 transiting planets in roughly 500 000 stellar light curves. All of these were found either with the box least-squares algorithm or by visual inspection. Here we use our new transit least-squares (TLS) algorithm to search for additional planets around all K2 stars that are currently known to host at least one planet. We discover and statistically validate 17 new planets with radii ranging from about 0.7 Earth radii (R⊕) to roughly 2.2 R⊕ and a median radius of 1.18 R⊕. EPIC 201497682.03, with a radius of 0.692+0.059−0.048, is the second smallest planet ever discovered with K2. The transit signatures of these 17 planets are typically 200 ppm deep (ranging from 100 ppm to 2000 ppm), and their orbital periods extend from about 0.7 d to 34 d with a median value of about 4 d. Fourteen of these 17 systems only had one known planet before, and they now join the growing number of multi-planet systems. Most stars in our sample have subsolar masses and radii. The small planetary radii in our sample are a direct result of the higher signal detection efficiency that TLS has compared to box-fitting algorithms in the shallow-transit regime. Our findings help in populating the period-radius diagram with small planets. Our discovery rate of about 3.7% within the group of previously known K2 systems suggests that TLS can find over 100 additional Earth-sized planets in the data of the Kepler primary mission.
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Haaland, David M., and David K. Melgaard. "New Classical Least-Squares/Partial Least-Squares Hybrid Algorithm for Spectral Analyses." Applied Spectroscopy 55, no. 1 (January 2001): 1–8. http://dx.doi.org/10.1366/0003702011951353.

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Qian Xiaofan, 钱晓凡, 饶帆 Rao Fan, 李兴华 Li Xinghua, 林超 Lin Chao, and 李斌 Li Bin. "Accurate Least-Squares Phase Unwrapping Algorithm." Chinese Journal of Lasers 39, no. 2 (2012): 0209001. http://dx.doi.org/10.3788/cjl201239.0209001.

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Al-Saggaf, Ubaid M., Muhammad Moinuddin, Muhammad Arif, and Azzedine Zerguine. "The q-Least Mean Squares algorithm." Signal Processing 111 (June 2015): 50–60. http://dx.doi.org/10.1016/j.sigpro.2014.11.016.

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Dissertations / Theses on the topic "Least squares algorithm"

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Guo, Hengdao. "Frequency Tracking and Phasor Estimation Using Least Squares and Total Least Squares Algorithms." UKnowledge, 2014. http://uknowledge.uky.edu/ece_etds/57.

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System stability plays an important role in electric power systems. With the development of electric power system, the scale of the electric grid is now becoming larger and larger, and many renewable energy resources are integrated in the grid. However, at the same time, the stability and safety issues of electric power system are becoming more complicated. Frequency and phasors are two critical parameters of the system stability. Obtaining these two parameters have been great challenges for decades. Researchers have provided various kinds of algorithms for frequency tracking and phasor estimation. Among them, Least Squares (LS) algorithm is one of the most commonly used algorithm. This thesis studies the LS algorithm and the Total Least Squares (TLS) algorithm working on frequency tracking and phasor estimation. In order to test the performance of the two algorithms, some simulations have been made in the Matlab. The Total Vector Error (TVE) is a commonly used performance criteria, and the TVE results of the two algorithms are compared. The TLS algorithm performs better than LS algorithm when the frequencies of all harmonic components are given.
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Kumar, Rajendra. "FAST FREQUENCY ACQUISITION VIA ADAPTIVE LEAST SQUARES ALGORITHM." International Foundation for Telemetering, 1986. http://hdl.handle.net/10150/615276.

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International Telemetering Conference Proceedings / October 13-16, 1986 / Riviera Hotel, Las Vegas, Nevada
A new least squares algorithm is proposed and investigated for fast frequency and phase acquisition of sinusoids in the presence of noise. This algorithm is a special case of more general adaptive parameter estimation techniques. The advantages of the algorithms are their conceptual simplicity, flexibility and applicability to general situations. For example, the frequency to be acquired can be time varying, and the noise can be non-gaussian, nonstationary and colored. As the proposed algorithm can be made recursive in the number of observations, it is not necessary to have a-priori knowledge of the received signal-to-noise ratio or to specify the measurement time. This would be required for batch processing techniques, such as the Fast Fourier Transform (FFT). The proposed algorithm improves the frequency estimate on a recursive basis as more and more observations are obtained. When the algorithm is applied in real time, it has the extra advantage that the observations need not be stored. The algorithm also yields a real time confidence measure as to the accuracy of the estimator.
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Tsakiris, Manolis. "On the regularization of the recursive least squares algorithm." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-21102010-101424/.

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This thesis is concerned with the issue of the regularization of the Recursive Least-Squares (RLS) algorithm. In the first part of the thesis, a novel regularized exponentially weighted array RLS algorithm is developed, which circumvents the problem of fading regularization that is inherent to the standard regularized exponentially weighted RLS formulation, while allowing the employment of generic time-varying regularization matrices. The standard equations are directly perturbed via a chosen regularization matrix; then the resulting recursions are extended to the array form. The price paid is an increase in computational complexity, which becomes cubic. The superiority of the algorithm with respect to alternative algorithms is demonstrated via simulations in the context of adaptive beamforming, in which low filter orders are employed, so that complexity is not an issue. In the second part of the thesis, an alternative criterion is motivated and proposed for the dynamical regulation of regularization in the context of the standard RLS algorithm. The regularization is implicitely achieved via dithering of the input signal. The proposed criterion is of general applicability and aims at achieving a balance between the accuracy of the numerical solution of a perturbed linear system of equations and its distance from the analytical solution of the original system, for a given computational precision. Simulations show that the proposed criterion can be effectively used for the compensation of large condition numbers, small finite precisions and unecessary large values of the regularization.
Esta tese trata da regularização do algoritmo dos mínimos-quadrados recursivo (Recursive Least-Squares - RLS). Na primeira parte do trabalho, um novo algoritmo array com matriz de regularização genérica e com ponderação dos dados exponencialmente decrescente no tempo é apresentado. O algoritmo é regularizado via perturbação direta da inversa da matriz de auto-correlação (Pi) por uma matriz genérica. Posteriormente, as equações recursivas são colocadas na forma array através de transformações unitárias. O preço a ser pago é o aumento na complexidade computacional, que passa a ser de ordem cúbica. A robustez do algoritmo resultante ´e demonstrada via simula¸coes quando comparado com algoritmos alternativos existentes na literatura no contexto de beamforming adaptativo, no qual geralmente filtros com ordem pequena sao empregados, e complexidade computacional deixa de ser fator relevante. Na segunda parte do trabalho, um critério alternativo ´e motivado e proposto para ajuste dinâmico da regularização do algoritmo RLS convencional. A regularização é implementada pela adição de ruído branco no sinal de entrada (dithering), cuja variância é controlada por um algoritmo simples que explora o critério proposto. O novo critério pode ser aplicado a diversas situações; procura-se alcançar um balanço entre a precisão numérica da solução de um sistema linear de equações perturbado e sua distância da solução do sistema original não-perturbado, para uma dada precisão. As simulações mostram que tal critério pode ser efetivamente empregado para compensação de números de condicionamento (CN) elevados, baixa precisão numérica, bem como valores de regularização excessivamente elevados.
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Degtyarena, Anna Semenovna. "The window least mean square error algorithm." CSUSB ScholarWorks, 2003. https://scholarworks.lib.csusb.edu/etd-project/2385.

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In order to improve the performance of LMS (least mean square) algorithm by decreasing the amount of calculations this research proposes to make an update on each step only for those elements from the input data set, that fall within a small window W near the separating hyperplane surface. This work aims to describe in detail the results that can be achieved by using the proposed LMS with window learning algorithm in information systems that employ the methodology of neural network for the purposes of classification.
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Kumar, Rajendra. "Differential Sampling for Fast Frequency Acquisition Via Adaptive Extended Least Squares Algorithm." International Foundation for Telemetering, 1987. http://hdl.handle.net/10150/615321.

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International Telemetering Conference Proceedings / October 26-29, 1987 / Town and Country Hotel, San Diego, California
This paper presents a differential signal model along with appropriate sampling techniques for least squares estimation of the frequency and frequency derivatives and possibly the phase and amplitude of a sinusoid received in the presence of noise. The proposed algorithm is recursive in measurements and thus the computational requirement increases only linearly with the number of measurements. The dimension of the state vector in the proposed algorithm does not depend upon the number of measurements and is quite small, typically around four. This is an advantage when compared to previous algorithms wherein the dimension of the state vector increases monotonically with the product of the frequency uncertainty and the observation period. Such a computational simplification may possibly result in some loss of optimality. However, by applying the sampling techniques of the paper such a possible loss in optimality can be made small.
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Wang, Dongmei. "Least mean square algorithm implementation using the texas instrument digital signal processing board." Ohio University / OhioLINK, 1999. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1175279376.

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Thanawalla, Rutang Kirit. "Valuation of gas swing options using an extended least squares Monte Carlo algorithm." Thesis, Heriot-Watt University, 2006. http://hdl.handle.net/10399/144.

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Wood, John D. "MIMO recursive least squares control algorithm for the AN/FPN-44A Loran-C transmitter." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA274820.

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Manmek, Thip Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Real-time power system disturbance identification and its mitigation using an enhanced least squares algorithm." Awarded by:University of New South Wales. Electrical Engineering and Telecommunications, 2006. http://handle.unsw.edu.au/1959.4/26233.

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This thesis proposes, analyses and implements a fast and accurate real-time power system disturbances identification method based on an enhanced linear least squares algorithm for mitigation and monitoring of various power quality problems such as current harmonics, grid unbalances and voltage dips. The enhanced algorithm imposes less real-time computational burden on processing the system and is thus called ???efficient least squares algorithm???. The proposed efficient least squares algorithm does not require matrix inversion operation and contains only real numbers. The number of required real-time matrix multiplications is also reduced in the proposed method by pre-performing some of the matrix multiplications to form a constant matrix. The proposed efficient least squares algorithm extracts instantaneous sine and cosine terms of the fundamental and harmonic components by simply multiplying a set of sampled input data by the pre-calculated constant matrix. A power signal processing system based on the proposed efficient least squares algorithm is presented in this thesis. This power signal processing system derives various power system quantities that are used for real-time monitoring and disturbance mitigation. These power system quantities include constituent components, symmetrical components and various power measurements. The properties of the proposed power signal processing system was studied using modelling and practical implementation in a digital signal processor. These studies demonstrated that the proposed method is capable of extracting time varying power system quantities quickly and accurately. The dynamic response time of the proposed method was less than half that of a fundamental cycle. Moreover, the proposed method showed less sensitivity to noise pollution and small variations in fundamental frequency. The performance of the proposed power signal processing system was compared to that of the popular DFT/FFT methods using computer simulations. The simulation results confirmed the superior performance of the proposed method under both transient and steady-state conditions. In order to investigate the practicability of the method, the proposed power signal processing system was applied to two real-life disturbance mitigation applications namely, an active power filter (APF) and a distribution synchronous static compensator (D-STATCOM). The validity and performance of the proposed signal processing system in both disturbance mitigations applications were investigated by simulation and experimental studies. The extensive modelling and experimental studies confirmed that the proposed signal processing system can be used for practical real-time applications which require fast disturbance identification such as mitigation control and power quality monitoring of power systems
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Shapero, Samuel Andre. "Configurable analog hardware for neuromorphic Bayesian inference and least-squares solutions." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/51719.

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Sparse approximation is a Bayesian inference program with a wide number of signal processing applications, such as Compressed Sensing recovery used in medical imaging. Previous sparse coding implementations relied on digital algorithms whose power consumption and performance scale poorly with problem size, rendering them unsuitable for portable applications, and a bottleneck in high speed applications. A novel analog architecture, implementing the Locally Competitive Algorithm (LCA), was designed and programmed onto a Field Programmable Analog Arrays (FPAAs), using floating gate transistors to set the analog parameters. A network of 6 coefficients was demonstrated to converge to similar values as a digital sparse approximation algorithm, but with better power and performance scaling. A rate encoded spiking algorithm was then developed, which was shown to converge to similar values as the LCA. A second novel architecture was designed and programmed on an FPAA implementing the spiking version of the LCA with integrate and fire neurons. A network of 18 neurons converged on similar values as a digital sparse approximation algorithm, with even better performance and power efficiency than the non-spiking network. Novel algorithms were created to increase floating gate programming speed by more than two orders of magnitude, and reduce programming error from device mismatch. A new FPAA chip was designed and tested which allowed for rapid interfacing and additional improvements in accuracy. Finally, a neuromorphic chip was designed, containing 400 integrate and fire neurons, and capable of converging on a sparse approximation solution in 10 microseconds, over 1000 times faster than the best digital solution.
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Books on the topic "Least squares algorithm"

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Karr, C. L. Genetic algorithm applied to least squares curve fitting. Washington, D.C: U.S. Dept. of the Interior, Bureau of Mines, 1990.

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Billings, S. A. Rational model identification using an extended least squares algorithm. Sheffield: University of Sheffield, Dept. of Control Engineering, 1990.

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Mao, K. Z. A regularized least squares algorithm for nonlinear rational model identification. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1996.

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Canada. Defence Research Establishment Atlantic. Least Squares Algorithm For Fitting Piecewise Linear Functions on Fixed Domains. S.l: s.n, 1985.

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Gould, N. I. M. A multidimensional filter algorithm for nonlinear equation and nonlinear least squares. Chilton: Rutherford Appleton Laboratory, 2003.

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Billings, S. A. Identification of nonlinear output-affine systems using an orthogonal least squares algorithm. Sheffield: University, Dept. of Control Engineering, 1987.

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Luo, W. The relationship between an orthogonal estimation algorithm and other least squares routines. Sheffield: University, Dept. of Control Engineering, 1988.

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Billings, S. A. Radial basis function network configuration using mutual information and the orthogonal least squares algorithm. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1995.

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Tobia, John. A time-varying analysis of the exponentially data weighted recursive least squares (EDW-RLS) algorithm. Ottawa: National Library of Canada, 1992.

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missing], [name. Least squares support vector machines. Singapore: World Scientific, 2002.

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Book chapters on the topic "Least squares algorithm"

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Alexander, S. Thomas. "The Least Squares Lattice Algorithm." In Adaptive Signal Processing, 142–53. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4612-4978-8_10.

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Alexander, S. Thomas. "The Least Mean Squares (LMS) Algorithm." In Adaptive Signal Processing, 68–86. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4612-4978-8_5.

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Guo, Hongbin, and Rosemary A. Renaut. "A Regularized Total Least Squares Algorithm." In Total Least Squares and Errors-in-Variables Modeling, 57–66. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-017-3552-0_6.

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Zhao, Ji, and Hongbin Zhang. "Projected Kernel Recursive Least Squares Algorithm." In Neural Information Processing, 356–65. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_38.

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Helwani, Karim. "Spatio-Temporal Regularized Recursive Least Squares Algorithm." In T-Labs Series in Telecommunication Services, 23–33. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08954-6_3.

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Macedo, Eloísa, and Adelaide Freitas. "The Alternating Least-Squares Algorithm for CDPCA." In Communications in Computer and Information Science, 173–91. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20352-2_12.

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Bunch, J. R., and R. C. LeBorne. "Analysis of the Recursive Least Squares Lattice Algorithm." In Linear Algebra for Large Scale and Real-Time Applications, 355–56. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-015-8196-7_25.

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Cantaluppi, Gabriele, and Giuseppe Boari. "A Partial Least Squares Algorithm Handling Ordinal Variables." In Springer Proceedings in Mathematics & Statistics, 295–306. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40643-5_22.

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Benesty, Jacob, Tomas Gänsler, Dennis R. Morgan, M. Mohan Sondhi, and Steven L. Gay. "A Robust Fast Recursive Least-Squares Adaptive Algorithm." In Digital Signal Processing, 55–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04437-7_3.

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Li, Yang, and Wanmei Tang. "A least Squares Support Vector Machine Sparseness Algorithm." In Lecture Notes in Electrical Engineering, 346–53. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2386-6_45.

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Conference papers on the topic "Least squares algorithm"

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Chansarkar, M. M., and U. B. Desai. "A robust recursive least squares algorithm." In Proceedings of ICASSP '93. IEEE, 1993. http://dx.doi.org/10.1109/icassp.1993.319527.

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Zhou, CanLin, Shuchun Si, XiaoLei Li, Zhenkun Lei, and YanJie Li. "Robust weighted least-squares phase-unwrapping algorithm." In Eleventh International Conference on Information Optics and Photonics (CIOP 2019), edited by Hannan Wang. SPIE, 2019. http://dx.doi.org/10.1117/12.2547663.

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Jiang, Qianru, Sheng Li, Zeru Lu, and Binbin Sun. "Block recursive least squares dictionary learning algorithm." In 2016 Chinese Control and Decision Conference (CCDC). IEEE, 2016. http://dx.doi.org/10.1109/ccdc.2016.7531304.

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Peng, Jing, and Alex J. Aved. "Approximate regularized least squares algorithm for classification." In Pattern Recognition and Tracking XXIX, edited by Mohammad S. Alam. SPIE, 2018. http://dx.doi.org/10.1117/12.2305075.

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Amblard, P. O., and H. Kadri. "Operator-valued kernel recursive least squares algorithm." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362810.

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Wenke Xu and Fuxiang Liu. "Recursive algorithm of Generalized Least Squares Estimator." In 2nd International Conference on Computer and Automation Engineering (ICCAE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccae.2010.5451430.

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Hongyu, Zhu, Huang Li, and Li Jing. "An optimal weighted least squares RAIM algorithm." In 2017 Forum on Cooperative Positioning and Service (CPGPS）. IEEE, 2017. http://dx.doi.org/10.1109/cpgps.2017.8075109.

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Liao, Chunhua, Jianqiang Du, Guohua Jin, and Chunlei Chen. "Improved Partial Least Squares Regression Recommendation Algorithm." In 2013 International Conference on Advanced Information Engineering and Education Science (ICAIEES 2013). Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/icaiees-13.2013.26.

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Yu-ting, Zhang, and Tan Zhi. "Optimization of least-squares localization algorithm in WSN." In 2014 26th Chinese Control And Decision Conference (CCDC). IEEE, 2014. http://dx.doi.org/10.1109/ccdc.2014.6852533.

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Jin, Wu. "A Genetic Algorithm for Spline Least Squares Calculations." In 2010 6th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2010. http://dx.doi.org/10.1109/wicom.2010.5600188.

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Reports on the topic "Least squares algorithm"

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Caponnetto, Andrea, and Ernesto De Vito. Fast Rates for Regularized Least-Squares Algorithm. Fort Belvoir, VA: Defense Technical Information Center, April 2005. http://dx.doi.org/10.21236/ada454989.

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DREWIEN, CELESTE A. A Parallel Prediction-Augmented Classical Least Squares/Partial Least Squares Hybrid Algorithm: CPLS 1.0 Code. Office of Scientific and Technical Information (OSTI), June 2000. http://dx.doi.org/10.2172/759455.

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Rokhlin, Vladimir, and Mark Tygert. A Fast Randomized Algorithm for Overdetermined Linear Least-Squares Regression. Fort Belvoir, VA: Defense Technical Information Center, April 2008. http://dx.doi.org/10.21236/ada489855.

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Leichner, S. A., G. B. Dantzig, and J. W. Davis. A strictly improving Phase 1 algorithm using least-squares subproblems. Office of Scientific and Technical Information (OSTI), April 1992. http://dx.doi.org/10.2172/10153254.

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Leichner, S. A., G. B. Dantzig, and J. W. Davis. A Strictly Improving Phase 1 Algorithm Using Least-Squares Subproblems. Fort Belvoir, VA: Defense Technical Information Center, April 1992. http://dx.doi.org/10.21236/ada251913.

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Leichner, S., G. Dantzig, and J. Davis. A strictly improving Phase 1 algorithm using least-squares subproblems. Office of Scientific and Technical Information (OSTI), April 1992. http://dx.doi.org/10.2172/5197680.

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Zikan, Karel. An Efficient Exact Algorithm for the 'Least Squares Image Registration Problem. Fort Belvoir, VA: Defense Technical Information Center, May 1989. http://dx.doi.org/10.21236/ada208725.

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Luk, Franklin T., and Sanzheng Qiao. Analysis of a Linearly Constrained Least Squares Algorithm for Adaptive Beamforming. Fort Belvoir, VA: Defense Technical Information Center, August 1992. http://dx.doi.org/10.21236/ada255017.

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Zikan, K. An efficient exact algorithm for the ''least squares'' image registration problems. Office of Scientific and Technical Information (OSTI), May 1989. http://dx.doi.org/10.2172/6125034.

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10

Caponnetto, Andrea, Lorenzo Rosasco, Ernesto De Vito, and Alessandro Verri. Empirical Effective Dimension and Optimal Rates for Regularized Least Squares Algorithm. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada466778.

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