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In this paper, we propose a parallel algorithm for a fund of fund (FOF) optimization model. Based on the structure of objective function, we create an augmented Lagrangian function and separate the quadratic term from the nonlinear term by the alternate direction multiplier method (ADMM), which creates two new subproblems that are much easier to be computed. To accelerate the convergence speed of the proposed algorithm, we use an adaptive step size method to adjust the step parameter according to the residual of the dual problem at every iterate. We show the parallelization of the proposed algorithm and implement it on CUDA with block storage for the structured matrix, which is shown to be up to two orders of magnitude faster than the CPU implementation on large-scale problems.

An ADMM Based Parallel Approach for Fund of Fund Construction