admm.sdp: Semidefinite Programming

In ADMM: Algorithms using Alternating Direction Method of Multipliers

Description

We solve the following standard semidefinite programming (SDP) problem

\textrm{min}_X ~ \textrm{tr}(CX)

\textrm{s.t.} A(X)=b, ~ X ≥q 0

with A(X)_i = \textrm{tr}(A_i^\top X) = b_i for i=1,…,m and X ≥q 0 stands for positive-definiteness of the matrix X. In the standard form, matrices C, A_1,A_2,…,A_m are symmetric and solution X would be symmetric and positive semidefinite. This function implements alternating direction augmented Lagrangian methods.

Usage

admm.sdp(
  C,
  A,
  b,
  mu = 1,
  rho = 1,
  abstol = 1e-10,
  maxiter = 496,
  print.progress = FALSE
)

Arguments

C	an (n\times n) symmetric matrix for cost.
A	a length-m list of (n\times n) symmetric matrices for constraint.
b	a length-m vector for equality condition.
mu	penalty parameter; positive real number.
rho	step size for updating in (0, \frac{1+√{5}}{2}).
abstol	absolute tolerance stopping criterion.
maxiter	maximum number of iterations.
print.progress	a logical; TRUE to show the progress, FALSE to go silent.

Value

a named list containing

x

a length-n solution vector

history

dataframe recording iteration numerics. See the section for more details.

Iteration History

When you run the algorithm, output returns not only the solution, but also the iteration history recording following fields over iterates,

objval

object (cost) function value

eps_pri

feasibility tolerance for primal feasibility condition

eps_dual

feasibility tolerance for dual feasibility condition

gap

gap between primal and dual cost function.

We use the stopping criterion which breaks the iteration when all eps_pri,eps_dual, and gap become smaller than abstol.

Author(s)

Kisung You

References

\insertRef

wen_alternating_2010aADMM

Examples

## a toy example
#  generate parameters
C  = matrix(c(1,2,3,2,9,0,3,0,7),nrow=3,byrow=TRUE)
A1 = matrix(c(1,0,1,0,3,7,1,7,5),nrow=3,byrow=TRUE)
A2 = matrix(c(0,2,8,2,6,0,8,0,4),nrow=3,byrow=TRUE)

A  = list(A1, A2)
b  = c(11, 19)

# run the algorithm
run = admm.sdp(C,A,b)
hst = run$history

# visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2))
plot(hst$objval,   type="b", cex=0.25, main="objective value")
plot(hst$eps_pri,  type="b", cex=0.25, main="primal feasibility")
plot(hst$eps_dual, type="b", cex=0.25, main="dual feasibility")
plot(hst$gap,      type="b", cex=0.25, main="primal-dual gap")
par(opar)

## Not run: 
## comparison with CVXR's result
require(CVXR)

#  problems definition
X = Variable(3,3,PSD=TRUE)
myobj = Minimize(sum_entries(C*X)) # objective
mycon = list(                      # constraint
  sum_entries(A[[1]]*X) == b[1],
  sum_entries(A[[2]]*X) == b[2]
)
myp = Problem(myobj, mycon)        # problem

# run and visualize
res  = solve(myp)
Xsol = res$getValue(X)

opar = par(no.readonly=TRUE)
par(mfrow=c(1,2), pty="s")
image(run$X, axes=FALSE, main="ADMM result")
image(Xsol,  axes=FALSE, main="CVXR result")
par(opar)

## End(Not run)

ADMM documentation built on Aug. 8, 2021, 9:07 a.m.