P. Comon, Tensor decompositionsstate of the art and applications, keynote address in ima conf, Mathematics in Signal Processing

F. L. Hitchcock, The expression of a tensor or a polyadic as a sum of products, J. Math. and Phys, vol.6, issue.1, pp.165-189, 1927.

R. A. Harshman, Foundations of the parafac procedure: Models and conditions for an" explanatory" multimodal factor analysis

R. A. Harshman, Determination and proof of minimum uniqueness condi-380 tions for parafac1, UCLA Working Papers in phonetics, vol.22, p.3, 1972.

J. D. Carroll and J. Chang, Analysis of individual differences in multidimensional scaling via an n-way generalization of eckart-young decomposition, Psychometrika, vol.35, issue.3, pp.283-319, 1970.

H. A. Kiers, Towards a standardized notation and terminology in multiway analysis, J. Chemometrics, vol.14, pp.105-122, 2000.

P. Comon, X. Luciani, and A. L. De-almeida, Tensor decompositions, alternating least squares and other tales, Journal of Chemometrics: A Journal of the Chemometrics Society, vol.23, issue.7-8, pp.393-405, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00410057

R. Bro, Parafac. tutorial and applications, Chemometrics and intelligent laboratory systems, vol.38, pp.149-171, 1997.
URL : https://hal.archives-ouvertes.fr/hal-02141162

K. R. Murphy, C. A. Stedmon, D. Graeber, and R. Bro, Fluorescence spectroscopy and multi-way techniques. parafac, Analytical Methods, vol.5, issue.23, pp.6557-6566, 2013.

C. Jutten and J. Herault, Blind separation of sources, part i: An adaptive algorithm based on neuromimetic architecture, Signal processing, vol.24, issue.1, pp.1-10, 1991.

A. Rouijel, K. Minaoui, P. Comon, and D. Aboutajdine, Cp decomposition approach to blind separation for ds-cdma system using a new performance 400 index, EURASIP Journal on Advances in Signal Processing, vol.2014, issue.1, p.128, 2014.

N. D. Sidiropoulos, G. B. Giannakis, and R. Bro, Blind parafac receivers for ds-cdma systems, IEEE Transactions on Signal Processing, vol.48, issue.3, 2000.

S. Sahnoun and P. Comon, Joint source estimation and localization, IEEE Transactions on Signal Processing, vol.63, issue.10, pp.2485-2495, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01005352

X. Liu, S. Bourennane, and C. Fossati, Denoising of hyperspectral images using the parafac model and statistical performance analysis, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.10, pp.3717-3724, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01280605

J. B. , Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics, Linear algebra and its applications, vol.18, issue.2, pp.95-138, 1977.

A. Stegeman and N. D. Sidiropoulos, On kruskals uniqueness condition for the candecomp/parafac decomposition, Linear Algebra and its applications, vol.415, issue.420, pp.540-552, 2007.

L. R. Tucker, Some mathematical notes on three-mode factor analysis, Psychometrika, vol.31, issue.3, pp.279-311, 1966.

G. H. Golub, Cf van loan, matrix computations

J. Kruskal, R. Harshman, and M. Lundy, How 3-mfa data can cause degenerate 420 parafac solutions, among other relationships, Multiway data analysis, pp.115-122, 1989.

M. Rajih, P. Comon, and R. A. Harshman, Enhanced line search: A novel method to accelerate parafac, SIAM journal on matrix analysis and applications, vol.30, issue.3, pp.1128-1147, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00327595

B. C. Mitchell and D. S. Burdick, Slowly converging parafac sequences: swamps and two-factor degeneracies, Journal of Chemometrics, vol.8, issue.2, pp.155-168, 1994.

W. S. Rayens and B. C. Mitchell, Two-factor degeneracies and a stabilization of parafac, Chemometrics and Intelligent Laboratory Systems, vol.38, issue.2, pp.173-181, 1997.

R. A. Harshman, The problem and nature of degenerate solutions or decompositions of 3-way arrays, Talk at the Tensor Decompositions Workshop, 2004.

P. Paatero, Construction and analysis of degenerate parafac models, Journal of Chemometrics: A Journal of the Chemometrics Society, vol.14, issue.3, 2000.

N. Li, S. Kindermann, and C. Navasca, Some convergence results on the regularized alternating least-squares method for tensor decomposition, Lin. Algebra Appl, vol.438, issue.2, pp.796-812, 2013.

E. Sanchez and B. R. Kowalski, Tensorial resolution: a direct trilinear decom-440 position, Journal of Chemometrics, vol.4, issue.1, pp.29-45, 1990.

S. Leurgans, R. Ross, and R. Abel, A decomposition for three-way arrays, SIAM Journal on Matrix Analysis and Applications, vol.14, issue.4, pp.1064-1083, 1993.

L. D. Lathauwer, A link between the canonical decomposition in multilinear algebra and simultaneous matrix diagonalization, SIAM journal on, p.445

, Matrix Analysis and Applications, vol.28, issue.3, pp.642-666, 2006.

C. A. Andersson and R. Bro, Improving the speed of multi-way algorithms:: Part i. tucker3, Chemometrics and intelligent laboratory systems, vol.42, pp.93-103, 1998.

R. Bro and C. A. Andersson, Improving the speed of multiway algorithms: Part 450 ii: Compression, Chemometrics and intelligent laboratory systems, vol.42, pp.105-113, 1998.

M. Rajih, P. Comon, and R. Harshman, Enhanced line search : A novel method to accelerate Parafac, SIAM Journal on Matrix Analysis Appl, vol.30, issue.3, pp.1148-1171, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00327595

V. Zarzoso and P. Comon, Robust independent component analysis, IEEE Trans. Neural Networks, vol.21, issue.2, pp.248-261, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00457300

R. C. Farias, J. H. De-morais-goulart, and P. Comon, Coherence constrained 460 alternating least squares, pp.613-617, 2018.

N. Parikh and S. Boyd, Proximal algorithms, vol.1, pp.127-239, 2014.

P. L. Combettes and J. Pesquet, Proximal splitting methods in signal pro-465 cessing, in: Fixed-point algorithms for inverse problems in science and engineering, pp.185-212, 2011.

M. V. Catalisano, A. V. Geramita, and A. Gimigliano, Ranks of tensors, secant varieties of Segre varieties and fat points, Linear Algebra Appl, vol.355, pp.263-285, 2002.

V. D. Silva and L. Lim, Tensor rank and the ill-posedness of the best low-rank approximation problem, SIAM Journal on Matrix Analysis Appl, vol.30, issue.3, pp.1084-1127, 2008.

P. Comon, Tensors: a brief introduction, IEEE Signal Processing Magazine, vol.31, issue.3, pp.44-53, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00923279

T. G. Kolda and B. W. Bader, Tensor decompositions and applications, SIAM review, vol.51, issue.3, pp.455-500, 2009.

D. L. Donoho and M. Elad, Optimally sparse representation in general (nonorthogonal) dictionaries via 1 minimization, Proceedings of the National Academy of Sciences, vol.100, issue.5, pp.2197-2202, 2003.

E. Candes and J. Romberg, Sparsity and incoherence in compressive sampling, Inverse problems, vol.23, issue.3, p.969, 2007.

E. J. Candès and T. Tao, The power of convex relaxation: Near-optimal matrix completion

R. Gribonval and M. Nielsen, Sparse representations in unions of bases, 2002.
URL : https://hal.archives-ouvertes.fr/inria-00570057

I. Domanov and L. D. Lathauwer, On the uniqueness of the canonical polyadic decomposition of third-order tensors-part i: Basic results and uniqueness of one factor matrix, SIAM Journal on Matrix Analysis and Applications, vol.34, issue.3, pp.855-875, 2013.

L. Lim and P. Comon, Blind multilinear identification, IEEE Transactions on Information Theory, vol.60, issue.2, pp.1260-1280, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00763275

M. H. Wright, Interior methods for constrained optimization, Acta numerica, vol.1, pp.341-407, 1992.

J. Gondzio, Interior point methods 25 years later, European Journal of 495 Operational Research, vol.218, issue.3, pp.587-601, 2012.

E. Lee and S. Waziruddin, Applying gradient projection and conjugate gradient to the optimum operation of reservoirs 1, JAWRA Journal of the American Water Resources Association, vol.6, issue.5, pp.713-724, 1970.

L. Zhang, W. Zhou, and D. Li, Global convergence of a modified fletcher-reeves 500 conjugate gradient method with armijo-type line search, Numerische Mathematik, vol.104, issue.4, pp.561-572, 2006.

A. Beck and M. Teboulle, A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM journal on imaging sciences, vol.2, issue.1, pp.183-202, 2009.

G. Tomasi and R. Bro, A comparison of algorithms for fitting the parafac model, Computational Statistics & Data Analysis, vol.50, issue.7, pp.1700-1734, 2006.