AD Model Builder/Cheat sheet

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ADMB Handy operations and functions (aka ADMB cheat sheet)

Adapted from a Word document written by Carolina Minte-Vera.

Let V, X, Y be vectors and vi, xi, yi be the elements of the vectors. Let M, N, A be matrices and mij, nij, aij be the elements of the matrices. Let α be a scalar.

Contents

Matrix and vector operations

Operation Math notation ADMB slow code ADMB fast code
Sum of two vectors vi = xi + yi V = X + Y X+=Y
Sum of scalar to a vector vi = xi + α V = X + nu X+=nu
Sum of two matrices aij = mij + nij A = M + N
Sum of a scalar to a matrix aij = mij + α A = M +alpha M+=alpha
Subtract two vectors vi = xi − yi V = X - Y X-=Y
Subtract a scalar from a vector vi = xi − α V=X-alpha X-=alpha
Subtract a scalar from a matrix vi = xi − α A=M-alpha M-=alpha
Subtract two matrices aij = mij − nij A=M-N M-=N
Vector dot product
α = ∑xiyi
i
 		alpha=X*Y
Outer product of two vectors mij = xiyj M=outer_prod(X,Y)
Multiply a scalar by a vector xi = αyi X=alpha*Y Y*=alpha
Multiply a vector by a scalar xi = yiα X=Y*alpha
Multiply a scalar by a matrix mij = αaij A=alpha*M M*=alpha
Multiply a vector by a matrix
xj = ∑yimij
i
 		X=Y*M
Multiply a matrix by a vector
xi = ∑mijyi
j
 		X=M*Y
Multiply two matrices
aij = ∑miknkj
k
 		A=M*N
Division of vector by scalar yi = xi / α Y=X/alpha X/=alpha
Division of scalar by a vector yi = α / xi Y=alpha/X
Division of a matrix by a scalar mij = aij / α M=N/alpha

Element–wise (e-w) operations in a matrix or vector object

Operation [need objects of the same dimension] Math notation ADMB code
Vectors e-w multiplication vi = xiyi V=elem_prod(X,Y)
Vectors e-w division vi = xi / yi V=elem_div(X,Y)
Matrices e-w multiplication mij = aijnij M=elem_prod(A,N)
Matrices e-w division mij = aij / nij M=elem_div(A,N)
Vectors e-w multiplication vi = xiyi V=elem_prod (X,Y)

Other matrix or vector operations

Operation Math notation ADMB code
Norm of a vector \alpha=\sqrt{\sum_{i} v_i^2} alpha=norm(V)
Norm square of a vector \alpha=\sum_{i} v_i^2 alpha=norm2(V)
Norm of a matrix \alpha=\sqrt{\sum_{i} \sum_{j} m_{ij}^2} alpha=norm(M)
Norm square of a matrix \alpha=\sum_{i} \sum_{j} m_{ij}^2 alpha=norm2(M)
Sum over elements of a vector
α = ∑vi
i
 		alpha=sum(V)
Row sums of a matrix object
xi = ∑mij
j
 		X=rowsum(M)
Column sum of a matrix object
yj = ∑mij
i
 		Y=colsum(M)
Concatenation X = (x1,x2,x3),Y = (y1,y2),V = (x1,x2,x3,y1,y2) V=X&Y

Still more matrix or vector operations

Operation ADMB code
Determinant (must be a square matrix cols=rows) alpha=det(M)
Logarithm of the determinant? (must be a square matrix cols=rows), sgn is an integer. Not explained in the manual, but used in the codes. alpha=ln_det(M,sgn)
Inverse (must be a square matrix cols=rows) N=inv(M)
Minimum value of a vector object alpha=min(V)
Maximum value of a vector object alpha=max(V)
Eigenvalues of a symmetric matrix V=Eigenvalues(M)
Eigenvectors of a symmetric matrix N=eigenvectors(M)
Identity matrix function M=identity_matrix(int min, int max)

Useful functions

Function Action
current_phase() return an integer that is the value of the current phase
last_phase() return a binary: "true" if the current phase is the last phase and "false" (=0) otherwise
Active(Par) return a binary: "true" if the parameter Par is active in the current phase and "false" (=0) otherwise
mceval_phase() return a binary: "true" if the current phase is the mceval phase and "false" (=0) otherwise
bolinha.initialize() Initializes (sets all elements equal to zero) the object bolinha

essay papers

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