algorithms

(noun)

In mathematics and computer science, an algorithm is a step-by-step procedure for calculations. Algorithms are used for calculation, data processing, and automated reasoning.

Related Terms

Examples of algorithms in the following topics:

  • Introduction to testing for independence in two-way tables (special topic)

    • 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 Knoke bureaucracies information exchange network analyzed by Tabu search

    • 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.

  • Continuous REGE for geodesic distances (Padgett's marriage data)

    • 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.

  • Problem-Solving

    • 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 step-by-step 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.

  • Multiplication of Complex Numbers

    • 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).

  • Expected counts in two-way tables

    • 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?

  • Problem Solving

    • 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."

  • Clustering similarities or distances profiles

    • Figure 13.12 shows a typical dialog for this algorithm.
    • For directed data, the algorithm will, by default, calculate similarities on the rows (out-ties) but not in-ties.
    • This algorithm also provides a more polished presentation of the result as a dendogram in a separate window, as shown in Figure 13.14.

  • Model selection exercises

    • In this exercise we consider a forward-selection algorithm and add variables to the model one-at-a-time.
    • In this exercise we consider a forward-selection algorithm and add variables to the model one-at-a-time.
    • However, since the adjusted R2 for the model with gestation is higher, it would be preferable to add gestation in the first step of the forward-selection algorithm.

  • Modeling Ecosystem Dynamics

    • Conceptual models describe ecosystem structure, while analytical and simulation models use algorithms to predict ecosystem dynamics.
    • Like analytical models, simulation models use complex algorithms to predict ecosystem dynamics.