  This paper studies the problem of finding an $(1+\epsilon)$-approximate
solution to positive semidefinite programs. These are semidefinite programs in
which all matrices in the constraints and objective are positive semidefinite
and all scalars are non-negative.
  We present a simpler \NC parallel algorithm that on input with $n$ constraint
matrices, requires $O(\frac{1}{\epsilon^3} log^3 n)$ iterations, each of which
involves only simple matrix operations and computing the trace of the product
of a matrix exponential and a positive semidefinite matrix. Further, given a
positive SDP in a factorized form, the total work of our algorithm is
nearly-linear in the number of non-zero entries in the factorization.
