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In mathematical optimization, Dantzig's simplex algorithm (or simplex method) is a popular algorithm for linear programming. [ 1 ] The name of the algorithm is derived from the concept of a simplex and was suggested by T. S. Motzkin . [ 2 ]
SIMPLE is an acronym for Semi-Implicit Method for Pressure Linked Equations. The SIMPLE algorithm was developed by Prof. Brian Spalding and his student Suhas Patankar at Imperial College London in the early 1970s. Since then it has been extensively used by many researchers to solve different kinds of fluid flow and heat transfer problems. [1]
The conjugate gradient method is often implemented as an iterative algorithm, applicable to sparse systems that are too large to be handled by a direct implementation or other direct methods such as the Cholesky decomposition. Large sparse systems often arise when numerically solving partial differential equations or optimization problems.
The simplest method is greedy line search, ... This algorithm can be significantly simplified by some lemmas, resulting in the conjugate gradient algorithm.
The revised simplex method is mathematically equivalent to the standard simplex method but differs in implementation. Instead of maintaining a tableau which explicitly represents the constraints adjusted to a set of basic variables, it maintains a representation of a basis of the matrix representing the constraints.
In mathematics and applied mathematics, perturbation theory comprises methods for finding an approximate solution to a problem, by starting from the exact solution of a related, simpler problem. [ 1 ] [ 2 ] A critical feature of the technique is a middle step that breaks the problem into "solvable" and "perturbative" parts. [ 3 ]
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The simple Dietz method [1] is a means of measuring historical investment portfolio performance, compensating for external flows into/out of the portfolio during the period. [2] The formula for the simple Dietz return is as follows: