In this sense, correlation attacks can be considered divide and conquer algorithms.

As is common for divide and conquer algorithms, we will use the Master theorem to analyze the running time.

Computational Geometry: Divide and conquer algorithms.

This method allows direct implementation of functions defined by mathematical induction and recursive divide and conquer algorithms.

Divide and Conquer Algorithms in Multidimensional Space, Ph.D. thesis.

It combines the design paradigms of divide and conquer algorithms, greedy algorithms, and randomized algorithms to achieve expected linear performance.

Divide and conquer algorithms can also be implemented by a non-recursive program that stores the partial sub-problems in some explicit data structure, such as a stack, queue, or priority queue.

In an algorithm of this sort (as for divide and conquer algorithms in general), it is desirable to use a larger base case in order to amortize the overhead of the recursion.

This process is an example of the general technique of divide and conquer algorithms; in many traditional implementations, however, the explicit recursion is avoided, and instead one traverses the computational tree in breadth-first fashion.

In the analysis of algorithms, the master theorem provides a cookbook solution in asymptotic terms (using Big O notation) for recurrence relations of types that occur in the analysis of many divide and conquer algorithms.

In computer science, the Akra-Bazzi method, or Akra-Bazzi theorem, is used to analyze the asymptotic behavior of the mathematical recurrences that appear in the analysis of divide and conquer algorithms where the sub-problems have substantially different sizes.

Divide and conquer algorithms are naturally adapted for execution in multi-processor machines, especially shared-memory systems where the communication of data between processors does not need to be planned in advance, because distinct sub-problems can be executed on different processors.

Highly-cited algorithmic research by Atallah includes papers on parallel and dynamic computational geometry, finding the symmetries of geometric figures, divide and conquer algorithms, and efficient parallel computations of the Levenshtein distance between pairs of strings.

Separator hierarchies may be used to devise efficient divide and conquer algorithms for planar graphs, and dynamic programming on these hierarchies can be used to devise exponential time and fixed-parameter tractable algorithms for solving NP-hard optimization problems on these graphs.