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Any randomized algorithm may be interpreted as a randomized choice among deterministic algorithms, and thus as a mixed strategy for Alice. Similarly, a non-random algorithm may be thought of as a pure strategy for Alice. In any two-player zero-sum game, if one player chooses a mixed strategy, then the other player has an optimal pure strategy ...
A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic or procedure. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, in the hope of achieving good performance in the "average case" over all possible choices of random determined by the random bits; thus either the running time, or the output (or both) are ...
Random optimization (RO) is a family of numerical optimization methods that do not require the gradient of the problem to be optimized and RO can hence be used on functions that are not continuous or differentiable. Such optimization methods are also known as direct-search, derivative-free, or black-box methods.
By applying it to a randomly-permuted graph that contains exactly one clique of size , and applying Yao's principle relating expected and average-case complexity, one can show that any deterministic or randomized algorithm for matroid parity that accesses its matroid only by independence tests needs to make an exponential number of tests.
The BO discriminatory pricing scheme is to offer one agent a price of $150 and the other agent a price of $100. Its expected revenue is 0.5*150 + 0.5*100 = $125. In practice, however, an auction is more complicated for the buyers since it requires them to declare their valuation in advance.
Dynamic pricing algorithms usually rely on one or more of the following data. Probabilistic and statistical information on potential buyers; see Bayesian-optimal pricing. Prices of competitors. E.g., a seller of an item may automatically detect the lowest price currently offered for that item, and suggest a price within $1 of that price. [1] [2 ...
Optimal RANSAC [4] was proposed to handle both these problems and is capable of finding the optimal set for heavily contaminated sets, even for an inlier ratio under 5%. Another disadvantage of RANSAC is that it requires the setting of problem-specific thresholds. RANSAC can only estimate one model for a particular data set.
Las Vegas algorithms were introduced by László Babai in 1979, in the context of the graph isomorphism problem, as a dual to Monte Carlo algorithms. [3] Babai [4] introduced the term "Las Vegas algorithm" alongside an example involving coin flips: the algorithm depends on a series of independent coin flips, and there is a small chance of failure (no result).