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In what follows, we propose a projection gradient optimization algorithm to solve the resulting minimization problems.
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The paper also proposes a projection strategy to integrate HDMR and MPS seamlessly.
[11] proposes a projection algorithm for solving the multi-valued variational inequality with a pseudomonotone mapping.
Recently, [15] proposes a projection algorithm for generalized variational inequality with pseudomonotone mapping.
Yet others have proposed a projection on regularly spaced vectors for HS pattern recognition [28].
We also proposed a projection model for Hadoop K-means workload.
This paper proposes a projection matrix design method using the support set of the sparse signal estimated via previous measurements.
Solodov [56] proposes a projection proximal point algorithm in a Hilbert space that finds the zeros of set-valued maximal monotone operators.
Furthermore, we proposed a projection analytical model to project performances and performance per watt with error deviation <10% between projected and measured data.
For the first method, Zhang et al. [9] proposed a spectral gradient method for problem (1.1) with (C=R^{n}), and Wang et al. [3] proposed a projection method for problem (1.1).
This paper proposes a projection matrix design algorithm using prior information on sparse signal to reduce local cumulative coherence, since small local coherence can improve the sparse signal recovery rate.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com