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  2. Range query (computer science) - Wikipedia

    en.wikipedia.org/wiki/Range_query_(computer_science)

    Given a function that accepts an array, a range query (,) on an array = [,..,] takes two indices and and returns the result of when applied to the subarray [, …,].For example, for a function that returns the sum of all values in an array, the range query ⁡ (,) returns the sum of all values in the range [,].

  3. Prefix sum - Wikipedia

    en.wikipedia.org/wiki/Prefix_sum

    Prefix sums are trivial to compute in sequential models of computation, by using the formula y i = y i − 1 + x i to compute each output value in sequence order. However, despite their ease of computation, prefix sums are a useful primitive in certain algorithms such as counting sort, [1] [2] and they form the basis of the scan higher-order function in functional programming languages.

  4. Summation - Wikipedia

    en.wikipedia.org/wiki/Summation

    where i is the index of summation; a i is an indexed variable representing each term of the sum; m is the lower bound of summation, and n is the upper bound of summation. The "i = m" under the summation symbol means that the index i starts out equal to m. The index, i, is incremented by one for each successive term, stopping when i = n. [b]

  5. Fenwick tree - Wikipedia

    en.wikipedia.org/wiki/Fenwick_tree

    A Fenwick tree or binary indexed tree (BIT) is a data structure that stores an array of values and can efficiently compute prefix sums of the values and update the values. It also supports an efficient rank-search operation for finding the longest prefix whose sum is no more than a specified value.

  6. Matrix addition - Wikipedia

    en.wikipedia.org/wiki/Matrix_addition

    The Kronecker sum is different from the direct sum, but is also denoted by ⊕. It is defined using the Kronecker product ⊗ and normal matrix addition. If A is n -by- n , B is m -by- m and I k {\displaystyle \mathbf {I} _{k}} denotes the k -by- k identity matrix then the Kronecker sum is defined by:

  7. Raising and lowering indices - Wikipedia

    en.wikipedia.org/wiki/Raising_and_lowering_indices

    Concretely, in the case where the vector space has an inner product, in matrix notation these can be thought of as row vectors, which give a number when applied to column vectors. We denote this by V ∗ := Hom ( V , K ) {\displaystyle V^{*}:={\text{Hom}}(V,K)} , so that α ∈ V ∗ {\displaystyle \alpha \in V^{*}} is a linear map α : V → K ...

  8. Matrix multiplication - Wikipedia

    en.wikipedia.org/wiki/Matrix_multiplication

    A coordinate vector is commonly organized as a column matrix (also called a column vector), which is a matrix with only one column. So, a column vector represents both a coordinate vector, and a vector of the original vector space. A linear map A from a vector space of dimension n into a vector space of dimension m maps a column vector

  9. Riemann sum - Wikipedia

    en.wikipedia.org/wiki/Riemann_sum

    If () = ([,]) (that is, the infimum of f over [,]), the method is the lower rule and gives a lower Riemann sum or lower Darboux sum. All these Riemann summation methods are among the most basic ways to accomplish numerical integration .