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function lookupByPositionIndex(i) node ← head i ← i + 1 # don't count the head as a step for level from top to bottom do while i ≥ node.width[level] do # if next step is not too far i ← i - node.width[level] # subtract the current width node ← node.next[level] # traverse forward at the current level repeat repeat return node.value end ...
A data structure known as a hash table.. In computer science, a data structure is a data organization and storage format that is usually chosen for efficient access to data. [1] [2] [3] More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data, [4] i.e., it is an algebraic structure about data.
In these languages, data and methods to manipulate the data are in the same code unit called an object. This encapsulation ensures that the only way that an object can access data is via methods of the object that contains the data. Thus, an object's inner workings may be changed without affecting code that uses the object.
The DPLL algorithm enhances over the backtracking algorithm by the eager use of the following rules at each step: Unit propagation If a clause is a unit clause , i.e. it contains only a single unassigned literal, this clause can only be satisfied by assigning the necessary value to make this literal true.
The NIST Dictionary of Algorithms and Data Structures [1] is a reference work maintained by the U.S. National Institute of Standards and Technology. It defines a large number of terms relating to algorithms and data structures. For algorithms and data structures not necessarily mentioned here, see list of algorithms and list of data structures.
The classic merge outputs the data item with the lowest key at each step; given some sorted lists, it produces a sorted list containing all the elements in any of the input lists, and it does so in time proportional to the sum of the lengths of the input lists. Denote by A[1..p] and B[1..q] two arrays sorted in increasing order.
Concurrent data structures are significantly more difficult to design and to verify as being correct than their sequential counterparts. The primary source of this additional difficulty is concurrency, exacerbated by the fact that threads must be thought of as being completely asynchronous: they are subject to operating system preemption, page faults, interrupts, and so on.
In computer science, a binomial heap is a data structure that acts as a priority queue.It is an example of a mergeable heap (also called meldable heap), as it supports merging two heaps in logarithmic time.