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gradient descent algorithm  (Stiefel)

 
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    Stiefel gradient descent algorithm
    Gradient Descent Algorithm, supplied by Stiefel, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/gradient-descent+algorithm/gradient+descent+algorithm/pm38932456-184-41-41
    Average 90 stars, based on 1 article reviews
    gradient descent algorithm - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    Variant Assay:

    Article Title: Automated Collective Variable Discovery for MFSD2A transporter from molecular dynamics simulations.
    Article Snippet: .. Here, we developed a variant of HLDA, termed GDHLDA (gradient descent-based HLDA) which takes advantage of the strengths of ZHLDA and MHLDA approaches by integrating the harmonic mean approaches for both within-class and between-class variances and, similar to ZHLDA, uses the Stiefel gradient descent algorithm for optimization. .. Specifically, in GDHLDA a constrained trace ratio optimization problem on the Stiefel manifold is considered (see Algorithm 1), with the objective of finding an orthonormal basis set (or projection matrix) W that minimizes the objective function J, given by: J(W) = ∑ ∑ nknl C l=k+1 C−1 k=1 Tr(WTSWW) Tr(WTBklW) s. t. WTW = I. (10) Here, C is the number of classes, ni is the count of data points in class i, SW is the within-class scatter matrix, and Bkl is the pairwise between-class scatter matrix, defined as follows: SW = (∑ Σk −1 C k=1 ) −1 (12) Bkl = (μk − μl)(μk − μl) T (13) where Σk is the covariance matrix which is defined as Σk = ∑ (x − μk)(x − μk) T x∈Ck (14) where Ck is class k and μk is the mean vector of class k.



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