Examples of algorithm in the following topics:

 Google is constantly running experiments to test new search algorithms.
 For example, Google might test three algorithms using a sample of 10,000 google.com search queries.
 Table 6.15 shows an example of 10,000 queries split into three algorithm groups.
 The group sizes were specified before the start of the experiment to be 5000 for the current algorithm and 2500 for each test algorithm.
 In this experiment, the explanatory variable is the search algorithm.

 The Network>Roles & Positions>Maximal Regular>Optimization algorithm seeks to sort nodes into (a user selected number of) categories that come as close to satisfying the "image" of regular equivalence as possible.
 Figure 15.9 shows the results of applying this algorithm to the Knoke information network.
 It is an iterative search algorithm, however, and can find local solutions.
 Many networks have more than one valid partitoning by regular equivalence, and there is no guarantee that the algorithm will always find the same solution.

 The way we solve problems can be influenced by algorithms, heuristics, intuition, insight, confirmation bias, and functional fixedness.
 Algorithms are mental processes which relate to how people understand, diagnose, and solve problems, mediating between a stimulus and response.
 A mathematical formula is a good example of an algorithm, as it has a straightforward and stepbystep way of being solved.
 Some of these mental processes include functional fixedness, confirmation bias, insight and intuition phenomenology, heuristics, and algorithms.
 Examine how algorithms, heuristics, intuition, insight, confirmation bias, and functional fixedness can influence judgment and decision making.

 By default, the algorithm extends the search to neighborhoods of distance 3 (though less or more can be selected).
 The continuous REGE algorithm applied to the undirected data is probably a better choice than the categorical approach.

 Note that the FOIL algorithm produces two real terms (from the First and Last multiplications) and two imaginary terms (from the Outer and Inner multiplications).

 If there really is no difference among the algorithms and 70.78% of people are satisfied with the search results, how many of the 5000 people in the "current algorithm" group would be expected to not perform a new search?
 That is, if there was no difference between the three groups, then we would expect 3539 of the current algorithm users not to perform a new search.
 Using the same rationale described in Example 6.35, about how many users in each test group would not perform a new search if the algorithms were equally helpful?

 Two of them, algorithms and heuristics, are of particularly great psychological importance.
 An algorithm is a series of sets of steps for solving a problem.
 Additionally, you need to know the algorithm (i.e., the complete set of steps), which is not usually realistic for the problems of daily life.
 The difference between an algorithm and a heuristic can be summed up in the example of trying to find a Starbucks (or some other national chain) in a city.
 An algorithm would be a series of steps: "Walk in an increasingly large grid pattern around the city blocks until you find a Starbucks or you have looked at every street."

 Algorithmic trading, now widely used by pension funds, mutual funds, and other institutional traders, is the use of electronic platforms to enter trading orders with an algorithm that calculates aspects such as timing, price, and quantity.
 Proponents have argued that algorithmic trading substantially improves market liquidity,while critics argue that this type of trading is opaque a "black box" and may contribute substantially to market volatility.

 Numerical integration constitutes a broad family of algorithms for calculating the numerical value of a definite integral.
 Numerical integration constitutes a broad family of algorithms for calculating the numerical value of a definite integral, and, by extension, the term is also sometimes used to describe the numerical solution of differential equations.

 Bioinformatics also deals with algorithms, databases and information systems, web technologies, artificial intelligence and soft computing, information and computation theory, structural biology, software engineering, data mining, image processing, modeling and simulation, discrete mathematics, control and system theory, circuit theory, and statistics.
 the development of new algorithms (mathematical formulas) and statistics with which to assess relationships among members of large data sets.
 Examples include pattern recognition, data mining, machine learning algorithms, and visualization.