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Binary search Visualization of the binary search algorithm where 7 is the target value Class Search algorithm Data structure Array Worst-case performance O (log n) Best-case performance O (1) Average performance O (log n) Worst-case space complexity O (1) Optimal Yes In computer science, binary search, also known as half-interval search, logarithmic search, or binary chop, is a search ...
Finds the size of a key/value pair table (associative array). For example, for {foo = 'foo', bar = 'bar'}, size will return 2. The function will also work on arrays, but for arrays it is more efficient to use the # operator. Note that to find the size, this function uses the pairs function to iterate through all of the keys.
Adds a todo list link to the head. The link will open a popup that allows you to view, add to, and modify your todo list. The todo list is stored in your user options, so it's private. 63: 24: ToggleSidebar : Adds a link to toggle the sidebar (only testing for Vector). N/A: N/A: Unslashifier
An example of a Bloom filter, representing the set {x, y, z} . The colored arrows show the positions in the bit array that each set element is mapped to. The element w is not in the set {x, y, z} , because it hashes to one bit-array position containing 0. For this figure, m = 18 and k = 3. An empty Bloom filter is a bit array of m bits, all set ...
Claim: If array A has length n, then permutations(n, A) will result in either A being unchanged, if n is odd, or, if n is even, then A is rotated to the right by 1 (last element shifted in front of other elements). Base: If array A has length 1, then permutations(1, A) will output A and stop, so A is unchanged. Since 1 is odd, this is what was ...
Exponential search allows for searching through a sorted, unbounded list for a specified input value (the search "key"). The algorithm consists of two stages. The first stage determines a range in which the search key would reside if it were in the list. In the second stage, a binary search is performed on this range.
For example, if the input is 123 456 789 and the hash table size 10 000, then squaring the key produces 15 241 578 750 190 521, so the hash code is taken as the middle 4 digits of the 17-digit number (ignoring the high digit) 8750. The mid-squares method produces a reasonable hash code if there is not a lot of leading or trailing zeros in the key.
Select – find the i-th smallest element stored in the tree; Rank(x) – find the rank of element x in the tree, i.e. its index in the sorted list of elements of the tree; Both operations can be performed in O(log n) worst case time when a self-balancing tree is used as the base data structure.