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When cyclic coordinate descent outperforms randomized coordinate descent
Published 2019“…The coordinate descent (CD) method is a classical optimization algorithm that has seen a revival of interest because of its competitive performance in machine learning applications. …”
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Quantum analytic descent
Published 2022-04-01“…Here we propose analytic descent: Given that the energy landscape must have a certain simple form in the local region around any reference point, it can be efficiently approximated in its entirety by a classical model—we support these observations with rigorous, complexity-theoretic arguments. …”
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Quantum analytic descent
Published 2022“…Here we propose analytic descent: Given that the energy landscape must have a certain simple form in the local region around any reference point, it can be efficiently approximated in its entirety by a classical model - we support these observations with rigorous, complexity-theoretic arguments. …”
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The curious case of testicular descent: factors controlling testicular descent with a note on cryptorchidism
Published 2023-03-01Subjects: Get full text
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Optimization by Adaptive Stochastic Descent.
Published 2018-01-01“…This paper outlines an optimization algorithm, Adaptive Stochastic Descent (ASD), that has been designed to replicate the essential aspects of manual parameter fitting in an automated way. …”
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Descent sets for oscillating tableaux
Published 2013-01-01“…The descent set of an oscillating (or up-down) tableau is introduced. …”
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Methodology of discovering the order of descent
Published 2020-05-01“…Considering the significance of the order of descent in the two knowledge of the Quranic sciences and the interpretation, the discovery of the ordinance of the revelation of the Holy Qur'an is a worthy study. …”
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Low-Rank Gradient Descent
Published 2023-01-01“…In this article, we leverage such low-rank structure to reduce the high computational cost of canonical gradient-based methods such as gradient descent (<monospace>GD</monospace>). Our proposed <italic>Low-Rank Gradient Descent</italic> (<monospace>LRGD</monospace>) algorithm finds an <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula>-approximate stationary point of a <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>-dimensional function by first identifying <inline-formula><tex-math notation="LaTeX">$r \leq p$</tex-math></inline-formula> significant directions, and then estimating the true <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>-dimensional gradient at every iteration by computing directional derivatives only along those <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> directions. …”
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Descent c-Wilf Equivalence
Published 2017-03-01“…For any $\sigma \in S_n$, we let $\mathrm{des}(\sigma)$ denote the number of descents of $\sigma$, $\mathrm{inv}(\sigma)$ denote the number of inversions of $\sigma$, and $\mathrm{LRmin}(\sigma)$ denote the number of left-to-right minima of $\sigma$. …”
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