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Sometimes this strategy is called the / stopping rule, because the probability of stopping at the best applicant with this strategy is already about / for moderate values of . One reason why the secretary problem has received so much attention is that the optimal policy for the problem (the stopping rule) is simple and selects the single best ...
There are generally two approaches to solving optimal stopping problems. [4] When the underlying process (or the gain process) is described by its unconditional finite-dimensional distributions , the appropriate solution technique is the martingale approach, so called because it uses martingale theory, the most important concept being the Snell ...
In decision theory, the odds algorithm (or Bruss algorithm) is a mathematical method for computing optimal strategies for a class of problems that belong to the domain of optimal stopping problems. Their solution follows from the odds strategy, and the importance of the odds strategy lies in its optimality, as explained below.
Flowchart of using successive subtractions to find the greatest common divisor of number r and s. In mathematics and computer science, an algorithm (/ ˈ æ l ɡ ə r ɪ ð əm / ⓘ) is a finite sequence of mathematically rigorous instructions, typically used to solve a class of specific problems or to perform a computation. [1]
The gradient descent can take many iterations to compute a local minimum with a required accuracy, if the curvature in different directions is very different for the given function. For such functions, preconditioning , which changes the geometry of the space to shape the function level sets like concentric circles , cures the slow convergence.
The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. [ 35 ] [ 36 ] Consequently, practical decision-tree learning algorithms are based on heuristics such as the greedy algorithm where locally optimal decisions are made at each node.
[4] [5] [6] Cramer's rule, implemented in a naive way, is computationally inefficient for systems of more than two or three equations. [7] In the case of n equations in n unknowns, it requires computation of n + 1 determinants, while Gaussian elimination produces the result with the same computational complexity as the computation of a single ...
[4] There are several versions of plurality voting for multi-member district. [5] The system that elects multiple winners at once with the plurality rule and where each voter casts as many X votes as the number of seats in a multi-seat district is referred to as plurality block voting.