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In order to avoid complex response surfaces, we constrained each smoothing effect to a maximum of five degrees of freedom.
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We also separately constrained each of the rates to be zero (q01 = 0 or q10 = 0).
To avoid excessively fine-scaled cubic splines, we constrained the smoothing parameter λ to values ≥4.
The estimates are constrained by smoothing, using methods such as natural cubic splines, polynomials, or stratified lag.
Also, ( {tilde{a}}_{q,W}^{ps} ) are assumed constant over a 24-h period whereas in the DC method the ( {tilde{delta}}_{WN}^{ps} ) is neither constrained nor smoothed.
Rassol and Bouaynaya [ 17] presented a new method based on constrained and smoothed Kalman filtering, which is capable of estimating time-varying networks from time-series data, including unobserved and noisy measurements.
To illustrate the practicality of the algorithms, we apply them to concrete constrained smooth convex optimization problems, such as constrained quadratic programming problems and generalized convex feasibility problems, and numerically compare them with previous algorithms based on the Krasnosel'skiĭ-Mann fixed point algorithm.
This model uses the Bayesian analysis and Monte Carlo methods to constrain the smoothing age depth model using the R statistical software package (cf., De Vleeschouwer et al. 2012; Shanahan et al. 2012).
The nonlinear conjugate gradient algorithms are a concrete response to the issue of constrained smooth convex optimization that is whether or not we can present nonlinear conjugate gradient algorithms to solve constrained smooth convex optimization problems.
The main contribution of this paper is to make a concrete response to an issue of constrained smooth convex optimization; that is, whether or not we can devise nonlinear conjugate gradient algorithms to solve constrained smooth convex optimization problems.
Shape COnstrained P-splines (SCOP-splines) (Pya and Wood 2015) were used for representation of the shape constrained smooth model terms.
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