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The golden-section search is a technique for finding an extremum (minimum or maximum) of a function inside a specified interval. For a strictly unimodal function with an extremum inside the interval, it will find that extremum, while for an interval containing multiple extrema (possibly including the interval boundaries), it will converge to one of them.
Since () is a sequence of nested intervals, the interval lengths get arbitrarily small; in particular, there exists an interval with a length smaller than . But from s ∈ I n {\displaystyle s\in I_{n}} one gets s − a n < s − σ {\displaystyle s-a_{n}<s-\sigma } and therefore a n > σ {\displaystyle a_{n}>\sigma } .
An indifference graph, formed from a set of points on the real line by connecting pairs of points whose distance is at most one. In graph theory, a branch of mathematics, an indifference graph is an undirected graph constructed by assigning a real number to each vertex and connecting two vertices by an edge when their numbers are within one unit of each other. [1]
The main objective of interval arithmetic is to provide a simple way of calculating upper and lower bounds of a function's range in one or more variables. These endpoints are not necessarily the true supremum or infimum of a range since the precise calculation of those values can be difficult or impossible; the bounds only need to contain the function's range as a subset.
There is no general definition of "close enough", but the criterion for convergence has to do with how "wiggly" the function is on the interval between the initial values. For example, if f {\displaystyle f} is differentiable on that interval and there is a point where f ′ = 0 {\displaystyle f'=0} on the interval, then the algorithm may not ...
A partition of an interval being used in a Riemann sum. The partition itself is shown in grey at the bottom, with the norm of the partition indicated in red. In mathematics, a partition of an interval [a, b] on the real line is a finite sequence x 0, x 1, x 2, …, x n of real numbers such that a = x 0 < x 1 < x 2 < … < x n = b.
If the bootstrap distribution of an estimator is symmetric, then percentile confidence-interval are often used; such intervals are appropriate especially for median-unbiased estimators of minimum risk (with respect to an absolute loss function). Bias in the bootstrap distribution will lead to bias in the confidence interval.
An interval graph is an undirected graph G formed from a family of intervals , =,,, … by creating one vertex v i for each interval S i, and connecting two vertices v i and v j by an edge whenever the corresponding two sets have a nonempty intersection.