To deal with the complexity issue of the BB algorithm, a heuristic algorithm is also proposed to provide a suboptimal solution for our MBIP problem by efficiently solving a particularly-constructed convex SDP problem.
On the downside, particle filters only provide a suboptimal solution, which converges in a statistical sense to the optimal solution.
The algorithms proposed in the STD, SSD, SA, and TPA steps provide a suboptimal solution which have low overhead and low computational complexity, and, as shown in Section 4 (Figs. 5, 6, 7, 8, 9, and 10), lead to better performance compared with an existing suboptimal algorithm.
The algorithms will still provide a suboptimal solution even when a filter is applied upstream.
PCA-ID [19] is a suboptimal solution to the learning problem.
This gives us an intuition to use Improved Backoff (IB) scheme for a suboptimal solution to incomplete cooperative game.
The proposed algorithm converges to a suboptimal solution to the original problem which also satisfies the Karush Kuhn Tucker conditions.
Again, only a suboptimal solution to the original problem can be achieved, but should the approximate substitute of the functions be good enough, the suboptimal solutions can be very close to the optimal, and therefore extremely useful.
We present iterative algorithms that find a suboptimal solution to the combinatorial problem by operating in two phases.
