Search results
Results from the WOW.Com Content Network
MATLAB (an abbreviation of "MATrix LABoratory" [18]) is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks.MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages.
In applied mathematics, test functions, known as artificial landscapes, are useful to evaluate characteristics of optimization algorithms, such as convergence rate, precision, robustness and general performance.
A practical way to enforce this is by requiring that the next search direction be built out of the current residual and all previous search directions. The conjugation constraint is an orthonormal-type constraint and hence the algorithm can be viewed as an example of Gram-Schmidt orthonormalization. This gives the following expression:
In computational fluid dynamics (CFD), the SIMPLE algorithm is a widely used numerical procedure to solve the Navier–Stokes equations. 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 ...
7.4 MATLAB code example. 8 See also. 9 Notes. 10 References. 11 ... One way to find the LU decomposition of this simple matrix would be to simply solve the linear ...
Let's consider the previous example and start from the code. ... GPL Matlab-implementation of RM-codes ; Source GPL Matlab-implementation of RM-codes; Weiss, E ...
LDPC codes functionally are defined by a sparse parity-check matrix. This sparse matrix is often randomly generated, subject to the sparsity constraints—LDPC code construction is discussed later. These codes were first designed by Robert Gallager in 1960. [5] Below is a graph fragment of an example LDPC code using Forney's factor graph notation.
For example, when = and =, Eq.3 equals , whereas direct evaluation of Eq.1 would require up to complex multiplications per output sample, the worst case being when both and are complex-valued. Also note that for any given M , {\displaystyle M,} Eq.3 has a minimum with respect to N . {\displaystyle N.} Figure 2 is a graph of the values of N ...