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Iterative reconstruction refers to iterative algorithms used to reconstruct 2D and 3D images in certain imaging techniques. For example, in computed tomography an image must be reconstructed from projections of an object. Here, iterative reconstruction techniques are usually a better, but computationally more expensive alternative to the common ...
Iterative design is a design methodology based on a cyclic process of prototyping, testing, analyzing, and refining a product or process. Based on the results of testing the most recent iteration of a design, changes and refinements are made.
Iterative and incremental development is any combination of both iterative design (or iterative method) and incremental build model for development. Usage of the term began in software development , with a long-standing combination of the two terms iterative and incremental [ 1 ] having been widely suggested for large development efforts.
The superiorization methodology is very general and has been used successfully in many important practical applications, such as iterative reconstruction of images from their projections, [1] [2] [3] single-photon emission computed tomography, [4] radiation therapy [5] [6] [7] and nondestructive testing, [8] just to name a few.
PDCA or plan–do–check–act (sometimes called plan–do–check–adjust) is an iterative design and management method used in business for the control and continual improvement of processes and products. [1] It is also known as the Shewhart cycle, or the control circle/cycle. Another version of this PDCA cycle is OPDCA. [2]
An early iterative method for solving a linear system appeared in a letter of Gauss to a student of his. He proposed solving a 4-by-4 system of equations by repeatedly solving the component in which the residual was the largest [ citation needed ] .
Random sample consensus (RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers, when outliers are to be accorded no influence [clarify] on the values of the estimates. Therefore, it also can be interpreted as an outlier detection method. [1]
Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for minimizing a differentiable multivariate function . The idea is to take repeated steps in the opposite direction of the gradient (or approximate gradient) of the function at the current point, because this is the direction of ...