Algorithmic

Last updated March 08, 2019

Algorithmic may refer to:

Algorithm, step-by-step instructions for a calculation
- Algorithmic art, art made by an algorithm
- Algorithmic composition, music made by an algorithm
- Algorithmic trading, trading decisions made by an algorithm
- Algorithmic patent, an intellectual property right in an algorithm
Algorithmic trading is a method of executing a large order using automated pre-programmed trading instructions accounting for variables such as time, price, and volume to send small slices of the order out to the market over time. They were developed so that traders do not need to constantly watch a stock and repeatedly send those slices out manually. Popular "algos" include Percentage of Volume, Pegged, VWAP, TWAP, Implementation Shortfall, Target Close. In the twenty-first century, algorithmic trading has been gaining traction with both retail and institutional traders. Algorithmic trading is not an attempt to make a trading profit. It is simply a way to minimize the cost, market impact and risk in execution of an order. It is widely used by investment banks, pension funds, mutual funds, and hedge funds because these institutional traders need to execute large orders in markets that cannot support all of the size at once.
Algorithmics, the science of algorithms
- Algorithmica , an academic journal for algorithm research
- Algorithmic efficiency, the computational resources used by an algorithm
- Algorithmic information theory, study of relationships between computation and information
- Algorithmic mechanism design, the design of economic systems from an algorithmic point of view
- Algorithmic number theory, algorithms for number-theoretic computation
- Algorithmic game theory, game-theoretic techniques for algorithm design and analysis
Algorithmica is a monthly peer reviewed, scientific journal, published by Springer Science+Business Media focused on research and application of computer science algorithms. The editor in chief is Ming-Yang Kao. Subject coverage includes sorting, searching, data structures, computational geometry, and linear programming, VLSI, distributed computing, parallel processing, computer aided design, robotics, graphics, data base design, and software tools.
In computer science, algorithmic efficiency is a property of an algorithm which relates to the number of computational resources used by the algorithm. An algorithm must be analyzed to determine its resource usage, and the efficiency of an algorithm can be measured based on usage of different resources. Algorithmic efficiency can be thought of as analogous to engineering productivity for a repeating or continuous process.
Algorithmic information theory is a subfield of information theory and computer science that concerns itself with the relationship between computation and information. According to Gregory Chaitin, it is "the result of putting Shannon's information theory and Turing's computability theory into a cocktail shaker and shaking vigorously."
Algorithmic cooling, a phenomenon in quantum computation
Algorithmic probability, a universal choice of prior probabilities in Solomonoff's theory of inductive inference

In mathematics and computer science, an algorithm is an unambiguous specification of how to solve a class of problems. Algorithms can perform calculation, data processing, and automated reasoning tasks.

Algorithmic art, also known as algorithm art, is art, mostly visual art, of which the design is generated by an algorithm. Algorithmic artists are sometimes called algorists.

Algorithmic composition is the technique of using algorithms to create music.

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In computational complexity theory, bounded-error probabilistic polynomial time (BPP) is the class of decision problems solvable by a probabilistic Turing machine in polynomial time with an error probability bounded away from 1/2 for all instances. BPP is one of the largest practical classes of problems, meaning most problems of interest in BPP have efficient probabilistic algorithms that can be run quickly on real modern machines. BPP also contains P, the class of problems solvable in polynomial time with a deterministic machine, since a deterministic machine is a special case of a probabilistic machine.

Discrete mathematics study of discrete mathematical structures

Discrete mathematics is the study of mathematical structures that are fundamentally discrete rather than continuous. In contrast to real numbers that have the property of varying "smoothly", the objects studied in discrete mathematics – such as integers, graphs, and statements in logic – do not vary smoothly in this way, but have distinct, separated values. Discrete mathematics therefore excludes topics in "continuous mathematics" such as calculus or Euclidean geometry. Discrete objects can often be enumerated by integers. More formally, discrete mathematics has been characterized as the branch of mathematics dealing with countable sets. However, there is no exact definition of the term "discrete mathematics." Indeed, discrete mathematics is described less by what is included than by what is excluded: continuously varying quantities and related notions.

Game theory is the study of mathematical models of strategic interaction between rational decision-makers. It has applications in all fields of social science, as well as in logic and computer science. Originally, it addressed zero-sum games, in which one person's gains result in losses for the other participants. Today, game theory applies to a wide range of behavioral relations, and is now an umbrella term for the science of logical decision making in humans, animals, and computers.

Computer science is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. One well known subject classification system for computer science is the ACM Computing Classification System devised by the Association for Computing Machinery.

Theoretical computer science subfield of computer science and of mathematics

Theoretical computer science (TCS) is a subset of general computer science and mathematics that focuses on more mathematical topics of computing and includes the theory of computation.

A computer scientist is a person who has acquired the knowledge of computer science, the study of the theoretical foundations of information and computation and their application.

In computer science, computational learning theory is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms.

Ray Solomonoff's theory of universal inductive inference is a theory of prediction based on logical observations, such as predicting the next symbol based upon a given series of symbols. The only assumption that the theory makes is that the environment follows some unknown but computable probability distribution. It is a mathematical formalization of Occam's razor and the Principle of Multiple Explanations.

A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, in the hope of achieving good performance in the "average case" over all possible choices of random bits. Formally, the algorithm's performance will be a random variable determined by the random bits; thus either the running time, or the output are random variables.

This article itemizes the various lists of mathematics topics. Some of these lists link to hundreds of articles; some link only to a few. The template to the right includes links to alphabetical lists of all mathematical articles. This article brings together the same content organized in a manner better suited for browsing.

The Bachelor of Computer Science or Bachelor of Science in Computer Science is a type of bachelor's degree, usually awarded after three or four years of collegiate study in computer science, but possibly awarded in fewer years depending on factors such as an institution's course requirements and academic calendar. In some cases it can be awarded in five years. In general, computer science degree programs emphasize the mathematical and theoretical foundations of computing.

Quantum neural networks (QNNs) are neural network models which are based on the principles of quantum mechanics. There are two different approaches to QNN research, one exploiting quantum information processing to improve existing neural network models, and the other one searching for potential quantum effects in the brain.

Statistical parsing is a group of parsing methods within natural language processing. The methods have in common that they associate grammar rules with a probability. Grammar rules are traditionally viewed in computational linguistics as defining the valid sentences in a language. Within this mindset, the idea of associating each rule with a probability then provides the relative frequency of any given grammar rule and, by deduction, the probability of a complete parse for a sentence. Using this concept, statistical parsers make use of a procedure to search over a space of all candidate parses, and the computation of each candidate's probability, to derive the most probable parse of a sentence. The Viterbi algorithm is one popular method of searching for the most probable parse.

The following outline is provided as a topical overview of science:

Algorithmic mechanism design (AMD) lies at the intersection of economic game theory and computer science.

Randomness lack of pattern or predictability in events, or, a measure of uncertainty of an outcome

Randomness is the lack of pattern or predictability in events. A random sequence of events, symbols or steps has no order and does not follow an intelligible pattern or combination. Individual random events are by definition unpredictable, but in many cases the frequency of different outcomes over a large number of events is predictable. For example, when throwing two dice, the outcome of any particular roll is unpredictable, but a sum of 7 will occur twice as often as 4. In this view, randomness is a measure of uncertainty of an outcome, rather than haphazardness, and applies to concepts of chance, probability, and information entropy.

In computational complexity theory, the linear search problem is an optimal search problem introduced by Richard E. Bellman..

Algorithmic

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