Potential method, the Glossary

Table of Contents

1. 25 relations: Absolute value, Amortized analysis, Array (data structure), Asymptotic computational complexity, Best, worst and average case, Big O notation, Binary number, Binary search tree, Bit numbering, Computational complexity theory, Counter (digital), Data structure, Dynamic array, Fibonacci heap, Hamming weight, Java (programming language), Potential energy, Priority queue, Python (programming language), Random access, Splay tree, Stack (abstract data type), Telescoping series, Transdichotomous model, Upper and lower bounds.

2. Analysis of algorithms

Absolute value

In mathematics, the absolute value or modulus of a real number x, is the non-negative value without regard to its sign.

See Potential method and Absolute value

Amortized analysis

In computer science, amortized analysis is a method for analyzing a given algorithm's complexity, or how much of a resource, especially time or memory, it takes to execute. Potential method and amortized analysis are analysis of algorithms.

See Potential method and Amortized analysis

Array (data structure)

In computer science, an array is a data structure consisting of a collection of elements (values or variables), of same memory size, each identified by at least one array index or key.

See Potential method and Array (data structure)

Asymptotic computational complexity

In computational complexity theory, asymptotic computational complexity is the usage of asymptotic analysis for the estimation of computational complexity of algorithms and computational problems, commonly associated with the usage of the big O notation.

See Potential method and Asymptotic computational complexity

Best, worst and average case

In computer science, best, worst, and average cases of a given algorithm express what the resource usage is at least, at most and on average, respectively. Potential method and best, worst and average case are analysis of algorithms.

See Potential method and Best, worst and average case

Big O notation

Big O notation is a mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. Potential method and Big O notation are analysis of algorithms.

See Potential method and Big O notation

Binary number

A binary number is a number expressed in the base-2 numeral system or binary numeral system, a method for representing numbers that uses only two symbols for the natural numbers: typically "0" (zero) and "1" (one).

See Potential method and Binary number

Binary search tree

In computer science, a binary search tree (BST), also called an ordered or sorted binary tree, is a rooted binary tree data structure with the key of each internal node being greater than all the keys in the respective node's left subtree and less than the ones in its right subtree.

See Potential method and Binary search tree

Bit numbering

In computing, bit numbering is the convention used to identify the bit positions in a binary number.

See Potential method and Bit numbering

Computational complexity theory

In theoretical computer science and mathematics, computational complexity theory focuses on classifying computational problems according to their resource usage, and relating these classes to each other.

See Potential method and Computational complexity theory

Counter (digital)

In digital logic and computing, a counter is a device which stores (and sometimes displays) the number of times a particular event or process has occurred, often in relationship to a clock.

See Potential method and Counter (digital)

Data structure

In computer science, a data structure is a data organization, and storage format that is usually chosen for efficient access to data.

See Potential method and Data structure

Dynamic array

In computer science, a dynamic array, growable array, resizable array, dynamic table, mutable array, or array list is a random access, variable-size list data structure that allows elements to be added or removed.

See Potential method and Dynamic array

Fibonacci heap

In computer science, a Fibonacci heap is a data structure for priority queue operations, consisting of a collection of heap-ordered trees.

See Potential method and Fibonacci heap

Hamming weight

The Hamming weight of a string is the number of symbols that are different from the zero-symbol of the alphabet used.

See Potential method and Hamming weight

Java (programming language)

Java is a high-level, class-based, object-oriented programming language that is designed to have as few implementation dependencies as possible.

See Potential method and Java (programming language)

Potential energy

In physics, potential energy is the energy held by an object because of its position relative to other objects, stresses within itself, its electric charge, or other factors.

See Potential method and Potential energy

Priority queue

In computer science, a priority queue is an abstract data type similar to a regular queue or stack abstract data type.

See Potential method and Priority queue

Python (programming language)

Python is a high-level, general-purpose programming language.

See Potential method and Python (programming language)

Random access

Random access (more precisely and more generally called direct access) is the ability to access an arbitrary element of a sequence in equal time or any datum from a population of addressable elements roughly as easily and efficiently as any other, no matter how many elements may be in the set.

See Potential method and Random access

Splay tree

A splay tree is a binary search tree with the additional property that recently accessed elements are quick to access again.

See Potential method and Splay tree

Stack (abstract data type)

In computer science, a stack is an abstract data type that serves as a collection of elements with two main operations.

See Potential method and Stack (abstract data type)

Telescoping series

In mathematics, a telescoping series is a series whose general term t_n is of the form t_n.

See Potential method and Telescoping series

Transdichotomous model

In computational complexity theory, and more specifically in the analysis of algorithms with integer data, the transdichotomous model is a variation of the random-access machine in which the machine word size is assumed to match the problem size.

See Potential method and Transdichotomous model

Upper and lower bounds

In mathematics, particularly in order theory, an upper bound or majorant of a subset of some preordered set is an element of that is every element of.

See Potential method and Upper and lower bounds

See also

Analysis of algorithms

• Accounting method (computer science)
• Adversary model
• Algorithmic efficiency
• Amortized analysis
• Analysis of algorithms
• Analysis of parallel algorithms
• Asymptotically optimal algorithm
• Average-case complexity
• Best, worst and average case
• Bidimensionality
• Big O notation
• Branching factor
• Cache-oblivious algorithm
• Cache-oblivious distribution sort
• Charging argument
• Combinatorial search
• Competitive analysis (online algorithm)
• Computational complexity
• Deterministic algorithm
• Dovetailing (computer science)
• Empirical algorithmics
• Entropy compression
• External memory algorithm
• Funnelsort
• Galactic algorithm
• Half-exponential function
• Instruction path length
• Iterative compression
• Kernelization
• Klee–Minty cube
• List update problem
• Master theorem (analysis of algorithms)
• Mem (computing)
• Memory-bound function
• Oblivious RAM
• Output-sensitive algorithm
• Parameterized complexity
• Polylogarithmic function
• Polynomial delay
• Potential method
• Probabilistic analysis of algorithms
• Pseudo-polynomial time
• Quasi-polynomial time
• Randomized algorithm
• Reservoir sampling
• Smoothed analysis
• The Art of Computer Programming
• Time complexity
• Worst-case complexity

References

[1] https://en.wikipedia.org/wiki/Potential_method