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Represent an Array Symbolically—Wolfram Documentation

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• See Also
  • VectorSymbol
  • MatrixSymbol
  • D
  • ArrayDot
  • NonThreadable
  • ArrayExpand
  • ArraySimplify
  • ComponentExpand
• Related Guides
  • Symbolic Vectors, Matrices and Arrays

ArraySymbol[a,{n₁,n₂,…},dom]

represents an array with elements in the domain dom.

ArraySymbol[a,{n₁,n₂,…},dom, sym]

represents an array with the symmetry sym.

• The name a in ArraySymbol[a,{n₁,n₂,…},dom, sym] can be any expression.
• Valid dimension specifications n_i in ArraySymbol[a,{n₁,n₂,…},dom, sym] are positive integers. It is also possible to work with symbolic dimension specifications.
• Element domain specifications dom in ArraySymbol[a,{n₁,n₂,…},dom, sym] include:
• Some symmetry specifications have names:
• In arithmetic and many other functions that work with lists, ArraySymbol objects do not automatically combine with other list arguments.
• Optimization functions, equation solvers and D recognize that ArraySymbol objects represent vector variables.

Assign the value of the variable a to represent an mnp array with name "a":

Arithmetic operations recognize that a is not a scalar:

D recognizes that a is an array variable:

Compute derivatives with respect to an array variable:

Compute derivatives involving array-valued functions:

Use an array variable in optimization:

Solve equations and inequalities involving an array variable:

Derive a least-squares solution for data given as a list of pairs :

Find the vector of vertical deviations for the data:

Define the sum of squares of the vertical deviations for the data:

Set up the least-squares equations:

Generate some data:

Solve the least-squares problem for this data:

Find an optimality condition for a portfolio optimization problem with the expected return and standard deviation :

The goal is to maximize when the vector of asset weights satisfies Total[x]=1. The constraint can be used to represent where the unconstrained vector variable consists of the first coordinates of :

The maximum occurs at a critical point of :

Express the condition in terms of :

Compute the gradient of the log-likelihood function of the linear regression model represented by the equation , where are normally distributed random variables with mean zero and variance :

The log-likelihood function is given by:

Compute :

Express the result in terms of :

Compute :

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