CVXR (version 0.99-7)

Variable-class: The Variable class.

Description

This class represents an optimization variable.

Usage

Variable(rows = 1, cols = 1, name = NA_character_)
# S4 method for Variable
as.character(x)
# S4 method for Variable
id(object)
# S4 method for Variable
is_positive(object)
# S4 method for Variable
is_negative(object)
# S4 method for Variable
size(object)
# S4 method for Variable
get_data(object)
# S4 method for Variable
name(object)
# S4 method for Variable
value(object)
# S4 method for Variable
value(object) <- value
# S4 method for Variable
grad(object)
# S4 method for Variable
variables(object)
# S4 method for Variable
canonicalize(object)

Arguments

rows

The number of rows in the variable.

cols

The number of columns in the variable.

name

(Optional) A character string representing the name of the variable.

x, object

A '>Variable object.

value

The value to assign to the primal variable.

Methods (by generic)

• id: The unique ID of the variable.

• is_positive: A logical value indicating whether the variable is positive.

• is_negative: A logical value indicating whether the variable is negative.

• size: The c(row, col) dimensions of the variable.

• get_data: Returns list(rows, cols, name).

• name: The name of the variable.

• value: The value of the variable.

• value<-: Set the value of the primal variable.

• grad: The sub/super-gradient of the variable represented as a sparse matrix.

• variables: Returns itself as a variable.

• canonicalize: The canonical form of the variable.

Slots

id

(Internal) A unique identification number used internally.

rows

The number of rows in the variable.

cols

The number of columns in the variable.

name

(Optional) A character string representing the name of the variable.

primal_value

(Internal) The primal value of the variable stored internally.

Examples

# NOT RUN {
x <- Variable(3, name = "x0") ## 3-int variable
y <- Variable(3, 3, name = "y0") # Matrix variable
as.character(y)
id(y)
is_positive(x)
is_negative(x)
size(y)
name(y)
value(y) <- matrix(1:9, nrow = 3)
value(y)
grad(y)
variables(y)
canonicalize(y)
# }