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Branch and cut [1] is a method of combinatorial optimization for solving integer linear programs (ILPs), that is, linear programming (LP) problems where some or all the unknowns are restricted to integer values. [2] Branch and cut involves running a branch and bound algorithm and using cutting planes to tighten the linear programming relaxations.
Data-flow analysis is a technique for gathering information about the possible set of values calculated at various points in a computer program.A program's control-flow graph (CFG) is used to determine those parts of a program to which a particular value assigned to a variable might propagate.
Different companies or organizations hold data analysis contests to encourage researchers to utilize their data or to solve a particular question using data analysis. [151] [152] A few examples of well-known international data analysis contests are as follows: [153] Kaggle competition, which is held by Kaggle. [154]
A variety of data re-sampling techniques are implemented in the imbalanced-learn package [1] compatible with the scikit-learn Python library. The re-sampling techniques are implemented in four different categories: undersampling the majority class, oversampling the minority class, combining over and under sampling, and ensembling sampling.
Computational thinking (CT) refers to the thought processes involved in formulating problems so their solutions can be represented as computational steps and algorithms. [1] In education, CT is a set of problem-solving methods that involve expressing problems and their solutions in ways that a computer could also execute. [2]
An algorithm is fundamentally a set of rules or defined procedures that is typically designed and used to solve a specific problem or a broad set of problems.. Broadly, algorithms define process(es), sets of rules, or methodologies that are to be followed in calculations, data processing, data mining, pattern recognition, automated reasoning or other problem-solving operations.
Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." [3] Exploratory data ...
Successive linear programming (SLP) — replace problem by a linear programming problem, solve that, and repeat; Sequential quadratic programming (SQP) — replace problem by a quadratic programming problem, solve that, and repeat; Newton's method in optimization. See also under Newton algorithm in the section Finding roots of nonlinear equations