stiefel.optSA: Simulated Annealing on Stiefel Manifold

In Riemann: Learning with Data on Riemannian Manifolds

Simulated Annealing on Stiefel Manifold

Description

Simulated Annealing is a black-box, derivative-free optimization algorithm that iterates via stochastic search in the neighborhood of current position. stiefel.optSA solves the following problem

\min_X f(X),\quad X \in St(p,k)

without any other auxiliary information such as gradient or hessian involved.

Usage

stiefel.optSA(func, p, k, ...)

Arguments

func	a function to be minimized.
p	dimension parameter as in St(k,p).
k	dimension parameter as in St(k,p).
...	extra parameters for SA algorithm including n.start number of runs; algorithm is executed n.start times (default: 5). stepsize size of random walk on each component (default: 0.1). maxiter maximum number of iterations for each run (default: 100). cooling triplet for cooling schedule. See the section for the usage. init.val if NULL, starts from a random point. Otherwise, a Stiefel matrix of size (p,k) should be provided for fixed starting point. print.progress a logical; if TRUE, it prints each iteration.

Value

a named list containing:

cost

minimized function value.

solution

a (p\times k) matrix that attains the cost.

accfreq

frequency of acceptance moves.

Examples

#-------------------------------------------------------------------
#               Optimization for Eigen-Decomposition
#
# Given (5x5) covariance matrix S, eigendecomposition is indeed 
# an optimization problem cast on the stiefel manifold. Here, 
# we are trying to find top 3 eigenvalues and compare.
#-------------------------------------------------------------------
## PREPARE
set.seed(121)                         # set seed
A = cov(matrix(rnorm(100*5), ncol=5)) # define covariance
myfunc <- function(p){                # cost function to minimize
  return(sum(-diag(t(p)%*%A%*%p)))
} 

## SOLVE THE OPTIMIZATION PROBLEM
Aout = stiefel.optSA(myfunc, p=5, k=3, n.start=40, maxiter=200)

## COMPUTE EIGENVALUES
#  1. USE SOLUTIONS TO THE ABOVE OPTIMIZATION 
abase   = Aout$solution
eig3sol = sort(diag(t(abase)%*%A%*%abase), decreasing=TRUE)

#  2. USE BASIC 'EIGEN' FUNCTION
eig3dec = sort(eigen(A)$values, decreasing=TRUE)[1:3]

## VISUALIZE
opar <- par(no.readonly=TRUE)
yran = c(min(min(eig3sol),min(eig3dec))*0.95,
         max(max(eig3sol),max(eig3dec))*1.05)
plot(1:3, eig3sol, type="b", col="red",  pch=19, ylim=yran,
     xlab="index", ylab="eigenvalue", main="compare top 3 eigenvalues")
lines(1:3, eig3dec, type="b", col="blue", pch=19)
legend(1, 1, legend=c("optimization","decomposition"), col=c("red","blue"),
       lty=rep(1,2), pch=19)
par(opar)

Riemann documentation built on March 18, 2022, 7:55 p.m.