02-05-2021



Jun
  1. Complex Matrix Plugin Wordpress
  2. Complex Matrix Plugin Java
  3. Complex Matrix Plugins

Multi-level marketing is a strategy that some direct sales companies use to encourage their existing distributors to recruit new distributors by paying the existing distributors a percentage of their recruits’ sales; the recruits are known as a distributor’s “downline.” All distributors also make money through direct sales of products to customers. Amway is an example of a well-known direct-sales company that uses multi-level marketing.

MLM Matrix

Out of all MLM compensation plans, the matrix model (also known as the forced matrix) is probably the simplest to understand. Looking, dare I say it, somewhat like a pyramid you start off at the top of your matrix and as you grow your organisation fill in allocated spots below you.

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There are of course slight variations that can be applied to the matrix model but overall the functionality is virtually the same. As your organisation grows the levels of your matrix increase and these levels provide you with residual income. This residual income is paid out in commissions via the product sales or recruitment those in your organisation achieve.

Your basic matrix model for an MLM compensation plan involves two size factors, A x B.

Usage: ComplexMatrix aa = new ComplexMatrix(bb); This creates a new instance of ComplexMatrix, aa, with a complex matrix that is a s a deep copy of the matrix in the existing Complex Matrix instance or Matrix instance, bb. See also setTwoDarray and copy.

A (Children Limit) is your frontline and is the number of levels wide your matrix is. It is comprised of members you personally recruit into your organisation.

B (Matrix Depth) is also a number and is the number of levels deep your matrix is. Some compensation plans cap this number whilst others don’t.

ComplexMatrix Plugin

Your standard MLM compensation plan matrix looks something like this:

The above is a 3X2 (2 levels deep and three levels wide) matrix and is a simple example. More complex matrix models can extend infinitely wide and deep or have limitations placed on the size so as to either force a matrix cycle or cap commission payouts.

The two biggest variations in matrix models are whether or not the matrix is fixed or not

The fixed matrix model

The fixed matrix model places limitations on your standard matrix, usually in the form of size restrictions. Typically there will be a cap in the form of A x B so as to trigger a cycle event.

A cycle is what happens when the matrix fills up and typically involves the person at the top of the matrix ‘cycling’ out and being rewarded with a commission payment.

After cycling the member is then placed into another existing matrix within the company, or at the top of a new fixed matrix. From here the member attempts to fill up the matrix again for another commission payout.

Fixed matrices are usually capped at small numbers (2 x 3 or 2 x 2 are quite common) so as to reward regular commission payouts for those who mass recruit into the business.

Due to the nature of the fixed model it is commonly affected by what’s called spillover. I’ll explain spillover a little later but the gist of it is your matrix can be faster filled off the work of your upline or those above you in a fixed matrix.

The standard matrix model

This standard variation of the matrix compensation model is a lot freer in that there are no restrictions on the width and depth of the matrix you create. Each person on your frontline spawns off an independent branch of your matrix and over time the idea is to grow these branches several levels deep.

Due to there being no restrictions on the size of your matrix, you can have as many people on your frontline and grow your organisation as many levels deep as you can achieve.

The standard matrix compensation model isn’t affected by spillover as each branch under your frontline run independent of each other. Because of this the unfixed matrix model initially relies more heavily on your own ability to fill it with recruits. In this sense team leverage is diminished in the early stages of building your organisation.

What is spillover?

When you join an MLM company with a fixed matrix compensation plan model, you’re placed at the top of a new matrix. This matrix however is a branch of your upline’s matrix, which in turn is a branch of their upline.

Spillover is what happens when someone above you in a parent matrix fills up a spot on their matrix which corresponds to a spot in your downline on your matrix.

Say for example you’re on the frontline of your uplines fixed matrix and your upline fills their frontline. The next level down (the frontline of your matrix) can then be filled over time by the recruiting efforts of your upline as they fill up the other levels of their own matrix.

Complex Matrix Plugin Wordpress

Here’s an example;

In the above matrix you can see how your own matrix fits in with that of your upline. For spillover to occur, let’s say your upline has filled their frontline (spots A and B), which you are on.

You directly recruit someone to spot C and then your upline manages to recruit another member. Due to your frontline being on their matrix spot D is filled via your upline. Spot D being filled affects both your own frontline and the matrix of your upline.

This affect is called spillover and requires no effort on your part to fill a spot on your own fixed matrix.

UniLevel MLM Plan

The MLM Uni level Plan running many Network Marketing Companies and providing an opportunity for the group or individual to earn huge profit.

One important thing with this MLM Uni level Plan is its simplicity; so that the MLM networker or company can explain Unilevel plan easily to their new comers who willing to join MLM business.

Uni-level Plan permits the affiliates to introduce new comers in its front line. There is no restriction for width i.e. a members can sponsor unlimited in width under his/her frontline and compensation distributed up to the limited depth. Further all frontline also efforts to do the same for earn bonuses or compensation.

To become more attractive Uni level Plan, MLM Company can introduce some rewards or incentives whenever a member introduced a set number of frontline.

Binary MLM Plan

Complex matrix plugin free

The MLM Binary Plan is a most popular plan among MLM companies, network marketers, part-timers and members who want to earn through MLM business.

MLM (multi-level marketing) companies where new joiners introduced into Binary Tree structure i.e. one on left and another on right sub-tree. Generally, one side sub-tree is referred to as Power leg while other is Profit leg.

Power leg grows with new member placement, even introduced by previously enrolled or ancestors. New members in the power leg placed under a leaf available node of the binary tree, when a member works to grow his Profit leg, some compensation distributed calculated by a formula using certain value matched with Power leg that may be 1:1 or 2:1.

This add-in for Excel 2000/XP is composed by 4 files:
  • matrix.xla
  • matrix.hlp
  • matrix.csv (*)
  • FunCustomize.dll (**)

(*) 'matrix.csv' can be used only if you have XNUMBERS 2.4 package. In that case put the CSV file in the same directory of xnumbers. The Xnumbers function handbook will be able to load also the new functions of 'matrix.xla'
(**) appears by courtesy of Laurent Longre (http://longre.free.fr)

It is available for download at The downloads section at (the sites of matrix.xla and FunCustomize areno longer available).
http://www.bowdoin.edu/~rdelevie/excellaneous/
that contains also other applications from the same team(Leonardo Volpi; John Beyers).
Unfortunately, if it is not my fault, some problems arisewhen working with this add-in in recent versions of Excel. However, since thecode is open you can access the VBA code and import the desired routines toyour workbook or even to an add-in.
In some cases, the routines import process is notstraightforward since there are routines that call other routines, implying thenecessity of tracing the computation flow. Another option that seem to work isthe importation of the relevant VBA modules.

Complex Matrix Plugin Java

Another limitation is that, as it seems, Microsoft ceased tosupport the old format help files. To sidestep this situation you can use theadd-in tutorials that are available in several sites, namely
Volume 1
https://msu.edu/course/fw/877/bence/matrix_1.8/MatrixTutorial.pdf
Volume 2
www.cs.bsu.edu/homepages/kerryj/kjones/MatrixTutorial2.pdf

You can also covert the add-in (xla) to a Workbook format performing the steps indicated in

Complex Matrix Plugins

  • Press Alt F11
  • Press Ctrl R
  • In the window pane click the 'Thisworkbook' object
  • Press F4
  • Scroll down this window until you see
  • IsAddin
  • Change the property to False
  • Now save the workbook as xls

The workbook obtained has a sheet with detailed information about the routines listed below. As far as I understand, these are only the master routines that call auxiliary routines not listed.
One simple way to get all procedures listed is
  • Copy the entire codes to an Excel spreadsheet column
  • Sort the contents of the column
  • Delete the rows that do not contain procedure declarations


NameFunction Description
Gauss_Jordan_stepGauss Jordan algorithm step by step
Gram_SchmidtGram-Schmidt's Orthonormalization
InterpolateInterpolation with polynomials
M_ABSEuclidean Norm of vector or matrix
M_ADDAddition of matrices
M_BABSimilarity transform [B]*[A]*[B]^-1
M_DETDeterminant
M_DET_CDeterminant for complex matrix
M_DET3Determinant for tridiagonal matrices
M_DIAGDiagonal matrix from a vector
M_DIAG_ERRDiagonalization error
M_EXPMatrix series expansion e^[M]
M_EXP_ERRTruncation error of matrix expansion series
M_IDMatrix Identity (I)
M_INVMatrix inverse [A]^-1
M_INV_CComplex Matrix inverse [A]^-1
M_MULT_CComplex matrices multiplication
M_MULT3Mutliplication for tridiagonal matrix
M_POWPower of matrix [A]^n
M_PRODProduct of matrices [A]*[B]*[C]*….
M_PROD_SMatrix multiplication for a scalar
M_RANKRank of matrix
M_SUBSubtraction of matrices
M_TMatrix transpose
M_TRACTrace
M_TRIA_ERRTriangolarization error
Mat_AdmReturns the Admittance matrix of a linear passive network
Mat_BlokPermReturns the permutation vector of block-partitioned matrix
Mat_BlokReturns the block-partitioned matrix
Mat_CholeskyCholesky decomposition
Mat_HessembergHessemberg form
Mat_HilbertReturns Hilbert's matrix
Mat_HouseholderReturns Houseolder matrix
Mat_LeontiefReturns the Leontief inverse matrix of Input Output Analysis
Mat_LULU decomposition
Mat_QRQR decomposition
Mat_QR_iterPerforms the diagonalization with the QR iterative method
Mat_TartagliaReturns Tartaglia's matrix
Mat_VandermondeReturns Vandermonde's matrix
MatCharPolyCharacteristic polynomial coefficients
MatCmpnCompanion matrix
MatCorrCorrelation matrix
MatCovarCovariance matrix
MatDiagExtrDiagonal extractor
MatEigenvalue_JacobiEigenvalues of symmetric matrix with Jacobi algorithm
MatEigenvalue_maxDominant eigenvectors with powers' method
MatEigenvalue_powEigenvectors with powers' method
MatEigenvalue_QLEigenvalues of tridiagonal matrix
MatEigenvalue_QREigenvalues with QR algorithm
MatEigenvalue_TridUniEigenvalues of tridiagonal uniform matrix
MatEigenvectorEigenvector of eigenvalue
MatEigenvector_CComplex eigenvector of eigenvalue
MatEigenvector_invEigenvector of eigenvalue
MatEigenvector_JacobiEigenvectors of symmetric matrix with Jacobi algorithm
MatEigenvector_maxDominant eigenvalues with powers' method
MatEigenvector_powEigenvalues with powers' method
MatEigenvector3Eigenvectors of tridiagonal matrix
MatExtractExtract sub-matrix
MatMopUpMatrix mop-up of round-off errors
MatNormVector or Matrix Norm
MatNormalizeVectors Normalization
MatOrtNormOrthonormalization
MatPermPermutation matrix
MatRndRandom matrix
MatRndEigRandom matrix with given eigenvalues
MatRndEigSymRandom symmetric matrix with given eigenvalues
MatRndRankRandom matrix with given rank or determinant
MatRndSimRandom symmetric matrix with given rank or det.
MatRotReturns the orthogonal planar rotation matrix
MatRotation_JacobiJacobi's rotation matrix
Path_FloydAll-pairs-path of Graph with Floyd's algorithm
Path_MinReturns the shortest path of a Graph with Floyd's algorithm
Poly_RootsPolynomial rootfinder with Lin-Bairstow method
Poly_Roots_QRPolynomial rootfinder with QR method
ProdScalScalar Product (inner)
ProdScal_CComplex scalar product
ProdVectVector Product 3D
REGRLLinear regression with SVD
REGRPPolynomial regression
RRMSroot mean squares
SimplexLinear Optimization with Simplex method
SVD_DSingular Value Decomposition [U]*[D]*[V]^t: returns D
SVD_USingular Value Decomposition [U]*[D]*[V]^t: returns U
SVD_VSingular Value Decomposition [U]*[D]*[V]^t: returns V
SYSLINSolve Linear System [A]x=b
SYSLIN_CSolve a Complex Linear System [A]x=b
SYSLIN_ITER_GSolve Linear System with Gauss-Seidel algorithm
SYSLIN_ITER_JSolve Linear System with Jacobi algorithm
SYSLIN_TSolve triangular linear sistem
SYSLIN3Solve tridiagonal linear system
SYSLINSINGSolve Singular Linear System [A]x=b
TRASFLINLinear Transform
VarimaxIndexReturns the Varimax index of a given Factors matrix
VarimaxRotComputes the orthogonal rotation with Varimax Kaiser's
M_MULT_TPZMultiplies a Toeplitz matrix for a vector
SYSLIN_TPZSolve Toeplitz Linear System [A]x=b
M_TPZ_ERRToeplitz matrix error