The Weighted Nonnegative Least-Squares Problem with Implicitly Characterized Points

References

• Bertsekas DP (2009) Convex Optimization Theory (Athena Scientific, Belmont, CA).Google Scholar
• Boutsidis C, Drineas P (2009) Random projections for the non-negative least-squares problem. Linear Algebra Appl. 431(5–7):760–771.Crossref, Google Scholar
• Demyanov V, Rubinov A (1970) Approximate Methods in Optimization Problems (American Elsevier Publishing, New York).Google Scholar
• Fattahi A, Dasu S, Ahmadi R (2017) Mass customization and parts’ capacity planning problem. Working paper, University of California, Los Angeles, Los Angeles.Google Scholar
• Fattahi A, Dasu S, Ahmadi R (2019) Mass customization and forecasting options’ penetration rates problem. Oper. Res. Forthcoming.Link, Google Scholar
• Franc V, Hlavac V, Navara M (2005) Sequential coordinate-wise algorithm for the non-negative least squares problem. Felsberg M, Heyden A, Krüger N, eds. Computer Analysis of Images and Patterns (Springer, Berlin), 407–414.Crossref, Google Scholar
• Frank M, Wolfe P (1956) An algorithm for quadratic programming. Naval Res. Logist. Q. 3(1/2):95–110.Crossref, Google Scholar
• Hazan E, Kale S (2012) Projection-free online learning. Working paper, Technion, Haifa, Israel.Google Scholar
• Jaggi M (2013) Revisiting Frank-Wolfe: Projection-free sparse convex optimization. PMLR 28:427–435.Google Scholar
• Lacoste-Julien S, Jaggi M (2015) On the global linear convergence of Frank-Wolfe optimization variants. Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 28 (Curran Associates, Red Hook, NY), 496–504.Google Scholar
• Lafond J, Wai HT, Moulines E (2015) On the online Frank-Wolfe algorithms for convex and non-convex optimizations. Working paper, Institut Mines-Télécom, Paris.Google Scholar
• Lawson CL, Hanson RJ (1995) Solving Least Squares Problems (Society for Industrial and Applied Mathematics, Philadelphia).Crossref, Google Scholar
• Potluru VK (2012) Frugal coordinate descent for large-scale NNLS. Proc. 26th AAAI Conf. Artificial Intelligence (AAAI Press, Menlo Park, CA), 2451–2452.Google Scholar
• Reddi SJ, Sra S, Poczos B, Smola A (2016) Stochastic Frank-Wolfe methods for nonconvex optimization. Working paper, Carnegie Mellon University, Pittsburgh.Google Scholar