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In Computer Science, Optimal Computing Budget Allocation (OCBA) is a simulation optimization method designed to maximize the Probability of Correct Selection (PCS) while minimizing computational costs. First introduced by Dr. Chun-Hung Chen in the mid-1990s, OCBA determines how many simulation runs (or how much computational time) or the number ...
The optimal answer requires 73 master rolls and has 0.401% waste; it can be shown computationally that in this case the minimum number of patterns with this level of waste is 10. It can also be computed that 19 different such solutions exist, each with 10 patterns and a waste of 0.401%, of which one such solution is shown below and in the picture:
In the design of experiments for estimating statistical models, optimal designs allow parameters to be estimated without bias and with minimum variance. A non-optimal design requires a greater number of experimental runs to estimate the parameters with the same precision as an optimal design. In practical terms, optimal experiments can reduce ...
The table shown on the right can be used in a two-sample t-test to estimate the sample sizes of an experimental group and a control group that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired significance level is 0.05. [4] The parameters used are:
This diagram shows an example corner solution where the optimal bundle lies on the x-intercept at point (M,0). IC 1 is not a solution as it does not fully utilise the entire budget, IC 3 is unachievable as it exceeds the total amount of the budget. The optimal solution in this example is M units of good X and 0 units of good Y.
The derivatives provide detailed information for such optimizers, but are even harder to calculate, e.g. approximating the gradient takes at least N+1 function evaluations. For approximations of the 2nd derivatives (collected in the Hessian matrix), the number of function evaluations is in the order of N².
Stratified sampling can yield that is smaller than 1 when using Proportionate allocation to strata sizes (when these are known a-priori, and correlated to the outcome of interest) or Optimum allocation (when the variance differs between strata and is known a-priori). [citation needed]
So, for every contribution of v to the algorithm welfare, the potential contribution to the optimal welfare could be at most 2v. Therefore, the optimal welfare is at most 2 times the algorithm welfare. The factor of 2 is tight for the greedy algorithm. For example, suppose there are two items x,y and the valuations are: {}