SAmove: SA-Move (Markov kernel)
In weiyaw/blackbox: A SMC-SA Algorithm for Constrained Optimisation

Performs a Markov transition step for each state. This function is intended for minimisation.

1	SAmove(theta, objf, proposal, temp, k, objv = NULL)

`theta`	a matrix with each column corresponding to a current state. (num mat)
`objf`	an objective function that takes a matrix with each column corresponds to a state, and return a vector with respective objective function values. (num mat -> num vec)
`proposal`	a random walk proposal that takes a matrix, and produces a matrix of proposed states, centred on the corresponding columns of the input matrix. It should also take in the current iteration count. ((num mat, num) -> num mat)
`temp`	temperature of the current iteration. (num)
`k`	current iteration count. (num)
`objv`	an optional vector specifying the objective function values for each column in `theta`. (num vec)

a list consisting of a matrix of transitioned states, a vector of objective function values corresponding to the transitioned states, and the acceptance proportion (useful for diagnostic purpose).

weiyaw/blackbox documentation built on June 7, 2019, 5:12 a.m.