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Trachtenberg defined this algorithm with a kind of pairwise multiplication where two digits are multiplied by one digit, essentially only keeping the middle digit of the result. By performing the above algorithm with this pairwise multiplication, even fewer temporary results need to be held. Example:
The pocket algorithm with ratchet (Gallant, 1990) solves the stability problem of perceptron learning by keeping the best solution seen so far "in its pocket". The pocket algorithm then returns the solution in the pocket, rather than the last solution.
An essential feature is the exploitation in some part of the algorithms of features derived from the mathematical model of the problems of interest, thus the definition "model-based heuristics" appearing in the title of some events of the conference series dedicated to matheuristics matheuristics web page.
The quantum algorithm solving Simon's problem, usually called Simon's algorithm, served as the inspiration for Shor's algorithm. [1] Both problems are special cases of the abelian hidden subgroup problem, which is now known to have efficient quantum algorithms. The problem is set in the model of decision tree complexity or query complexity and ...
God's algorithm is a notion originating in discussions of ways to solve the Rubik's Cube puzzle, [1] but which can also be applied to other combinatorial puzzles and mathematical games. [2] It refers to any algorithm which produces a solution having the fewest possible moves (i.e., the solver should not require any more than this number).
Can you vary or change your problem to create a new problem (or set of problems) whose solution(s) will help you solve your original problem? Search: Auxiliary Problem: Can you find a subproblem or side problem whose solution will help you solve your problem? Subgoal: Here is a problem related to yours and solved before: Can you find a problem ...
For example, it's quite possible to reduce a difficult-to-solve NP-complete problem like the boolean satisfiability problem to a trivial problem, like determining if a number equals zero, by having the reduction machine solve the problem in exponential time and output zero only if there is a solution. However, this does not achieve much ...
In each iteration of the method, we increase the penalty coefficient (e.g. by a factor of 10), solve the unconstrained problem and use the solution as the initial guess for the next iteration. Solutions of the successive unconstrained problems will asymptotically converge to the solution of the original constrained problem.