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QA-Learning reduces the complexity of large state space problems by decomposing the problem into more manageable sub-problems, and distributing agents between these sub-problems, to improve efficiency and enhance parallelisation [2].

We don't want to make the problem into more than it is.

The trick is to break the problem into more digestible components, while still linking each step to the other.

Similar(57)

The secret, as with any computing task, is to break the problem into smaller, more manageable chunks.

In particular, we recast the problem into a more tractable form and combine bracketing method (e.g., golden ratio search)[30, 31] with ADMM to derive the distributed algorithm for problem P2.

Load balancing in Charm++ applications like ChaNGa is normally achieved by over-decomposing the problem into many more objects than processors and letting the Charm++ dynamic load balancing framework balance the load by mapping the objects to processors (Zheng 2005).

Still, linear differential equation models have been fit efficiently to expression data by various means, most prominently by recasting the problem into other more convenient forms [ 9, 19, 20].

The Divide and Conquer pattern is applied first by recursively breaking down the problem into two or more sub-problems of the same type, until these become simple enough to be solved directly.

Therefore, it is questionable if the observed effects are due to the execution of arithmetic strategies or due to the translation of the problems into a more abstract representation that can then be subjected to familiar arithmetic problem solving strategies.

Next, the solvers decompose the problem specification into more manageable sub-problems.

In trying to overcome this obstacle, thus to speed up SPRIT, Hill et al. propose a divide-and-conquer-type reduction that breaks the problem into several smaller and more tractable subproblems before calculating the rSPR distance for each subproblem separately.

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