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trust-region algorithm of fminunc  (MathWorks Inc)


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    MathWorks Inc trust-region algorithm of fminunc
    Trust Region Algorithm Of Fminunc, supplied by MathWorks Inc, 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/fminuncs+trust-region+algorithm/pm37230907-110-6-9
    Average 90 stars, based on 1 article reviews
    trust-region algorithm of fminunc - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: Influences of familiarity and recollection on value-based decision-making.
    Article Snippet: In both cases, the functions were fit using the Matlab function ‘fminunc’ with a cost function based on the negative log likelihood estimated using a Gaussian probability density function.

    Article Title: Fair curve designing by Said-Ball curve.
    Article Snippet: The MATLAB built-in function “fminunc” is used to find the minimum of an unconstrained optimization problem.

    Article Title: Semiparametric regression analysis of interval-censored failure time data with a cure subgroup and nonignorable missing covariates.
    Article Snippet: This article discusses regression analysis of interval-censored failure time data in the presence of a cure fraction and nonignorable missing covariates.. To address the challenges caused by interval censoring, missing covariates and the existence of a cure subgroup, we propose a joint semiparametric modeling framework that simultaneously models the failure time of interest and the missing covariates.. In particular, we present a class of semiparametric nonmixture cure models for the failure time and a semiparametric density ratio model for the missing covariates.

    Article Title: Fair curve designing by Said-Ball curve.
    Article Snippet: The MATLAB “fminunc” finds a minimum of a function with severable variables.

    Article Title: Quantitative photoacoustic imaging using known chromophores as fluence marker
    Article Snippet: The GBM algorithm, using MATLAB’s fminunc, minimizes the error function with the supplied gradient.

    Article Title: Variable selection for uncertain regression models based on elastic net method
    Article Snippet: On one hand, determining the exact number of explanatory variables in uncertain regression analysis is often challenging, as real-world scenarios are interconnected.. On the other hand, it’s worth noting that even when the number of variables is determined, some variables may have minimal impact on the response variable, and retaining them would only increase the complexity of the model.. Therefore, variable selection methods can help eliminate variables with minimal impact to enhance the simplicity and interpretability of the model.

    Article Title: Moment Selection and Generalized Empirical Likelihood Estimation in High-Dimensional Unconditional Moment Conditions
    Article Snippet: This paper investigates the moment selection and parameter estimation problem of highdimensional unconditional moment conditions.. First, the authors propose a Fantope projection and selection (FPS) approach to distinguish the informative and uninformative moments in high-dimensional unconditional moment conditions.. Second, for the selected unconditional moment conditions, the authors present a generalized empirical likelihood (GEL) approach to estimate unknown parameters.

    Article Title:
    Article Snippet: The Hessian around the338 optimal value Hij = ∂θi∂θjL|~θ∗ is estimated for us during the optimization by MATLAB’s fminunc() using the finite differences method.339 Parameters have correlated effects on model fits.



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