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logcondens (version 2.1.8)

quadDeriv: Gradient and Diagonal of Hesse Matrix of Quadratic Approximation to Log-Likelihood Function L

Description

Computes gradient and diagonal of the Hesse matrix w.r.t. to \(\eta\) of a quadratic approximation to the reparametrized original log-likelihood function

$$L(\phi) = \sum_{i=1}^m w_i \phi(x_i) - \int_{-\infty}^{\infty} \exp(\phi(t)) dt. $$

where \(L\) is parametrized via

$${\bold{\eta}}({\bold{\phi}}) = \Bigl(\phi_1, \Bigl(\eta_1+ \sum_{j=2}^i (x_i-x_{i-1})\eta_i\Bigr)_{i=2}^m\Bigr).$$

\({\bold{\phi}}\): vector \((\phi(x_i))_{i=1}^m\) representing concave, piecewise linear function \(\phi\),
\({\bold{\eta}}\): vector representing successive slopes of \(\phi.\)

Usage

quadDeriv(dx, w, eta)

Value

\(m \times 2\) matrix. First column contains gradient and second column diagonal of Hesse matrix.

Arguments

dx

Vector \((0, x_i-x_{i-1})_{i=2}^m.\)

w

Vector of weights as in activeSetLogCon.

eta

Vector \({\bold{\eta}}.\)

See Also

quadDeriv is used by the function icmaLogCon.