A D.C. Optimization Algorithm for Solving the Trust-Region Subproblem

By Tao Pham Dinh, Hoai An Le Thi. 1998

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Citations in the corpus

Multiple-Source Adaptation with Domain Classifiers (2020, background): “Problem (7) can be solved by the DC (difference-of-convex) algorithm [Tao and An, 1997, 1998, Sriperumbudur and Lanckriet, 2012].”
Algorithms and Theory for Multiple-Source Adaptation (2018, background): “Our DC decompositions prove that the optimization problem (4) can be cast as the following variational form of a DC-programming problem [Tao and An, 1997, 1998, Sriperumbudur and Lanckriet, 2012]: The DC-programming algorithm works as follows.”
Multi-instance multi-label learning (2008, background): “Cccp is guaranteed to converge to a local minimum [75], and in many cases it can even converge to a global solution [25].”
Learning Theory and Algorithms for Forecasting Non-stationary Time Series (2015, methodology): “However, for the squared loss, our problem can be cast as an instance of the trust region problem, which can be solved globally using the DCA algorithm of Tao and An [1998]. Note that problem (4) is not jointly convex in q and w.”
Minimization of ℓ1-2 for Compressed Sensing (2015, methodology): “The DCA is a descent method without line search introduced by Tao and An [45, 46].”
L1−2 minimization for exact and stable seismic attenuation compensation (2018, methodology): “The DCA is a robust and efficient descent method introduced by Tao & An (1998), which copes with the minimization of an objective function F(x) = G(x) − H(x), where G(x) and H(x) are proper convex functions.”