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Implemented by quasi-Newton algorithm, a coordinate descent algorithm is developed to estimate the high-order coefficients of the translational motion.
Algorithm 1 (the coordinate descent algorithm for L 1/2 penalized network-constrained logistic model).
In this article, we propose a coordinate descent algorithm implemented by quasi-Newton algorithm.
With parameters specified, problem (4) can be solved efficiently by the block coordinate descent algorithm presented in Additional file 1: S4.3, Algorithm S1.
The coordinate descent algorithm is implemented by the quasi-Newton algorithm, yielding fast convergence.
And, a coordinate descent algorithm is proposed to solve the optimization implemented by quasi-Newton algorithm.
However, the monotone and fast convergence is usually achieved by the coordinate descent algorithm even under low SNR, and as we apply the quasi-Newton algorithm in the inner loop, its fast convergence also promotes the algorithm in efficiency.
□ In addition to Theorem 3.7, since the computation of L in the first step decreases the value of the objective in Equation (7), and the coordinate descent algorithm for updating W in the third step also monotonically decreases the value of the objective, the algorithm is guaranteed to converge.
ℓ 1 -based coordinate descent algorithm The problem in (17) is combinatorial and nonsmooth, thus a greedy strategy based on the coordinate descent algorithm and the weighted median estimator is proposed in [44].
Figure 2 Flowchart of the coordinate descent algorithm.
A novel coordinate descent algorithm is proposed to solve the minimum entropy optimization.
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