Expressions

CVXPY represents mathematical objects as expression trees. An expression tree is a collection of mathematical expressions linked together by one or more atoms. Expression trees are encoded as instances of the Expression class, and each Leaf in a tree is a Variable, Parameter, or Constant.

Expression

class cvxpy.expressions.expression.Expression

Bases: cvxpy.utilities.canonical.Canonical

A mathematical expression in a convex optimization problem.

Overloads many operators to allow for convenient creation of compound expressions (e.g., the sum of two expressions) and constraints.

T

Expression – The transpose of the expression.

__add__(other)

Expression : Sum two expressions.

__div__(other)

Expression : One expression divided by another.

__eq__(other)

Zero : Creates an equality constraint self == other.

__ge__(other)

NonPos : Creates an inequality constraint.

__le__(other)

NonPos : Creates an inequality constraint.

__lshift__(other)

PSD : Creates a negative semidefinite inequality.

__matmul__(other)

Expression : Matrix multiplication of two expressions.

__mul__(other)

Expression : The product of two expressions.

__pow__(power)

Raise expression to a power.

Parameters:power (float) – The power to which to raise the expression. Returns:The expression raised to power. Return type:Expression

__radd__(other)

Expression : Sum two expressions.

__rdiv__(other)

Expression : Called for Number / Expression.

__rlshift__(other)

PSD : Creates a negative semidefinite inequality.

__rmatmul__(other)

Expression : Called for matrix @ Expression.

__rmul__(other)

Expression : Called for Number * Expression.

__rrshift__(other)

PSD : Creates a positive semidefinite inequality.

__rshift__(other)

PSD : Creates a positive semidefinite inequality.

__rsub__(other)

Expression : The difference of two expressions.

__rtruediv__(other)

Expression : Called for Number / Expression.

__sub__(other)

Expression : The difference of two expressions.

__truediv__(other)

Expression : One expression divided by another.

curvature

str – The curvature of the expression.

domain

list – The constraints describing the closure of the region where the expression is finite.

grad

Gives the (sub/super)gradient of the expression w.r.t. each variable.

Matrix expressions are vectorized, so the gradient is a matrix.

Returns:A map of variable to SciPy CSC sparse matrix; None if a variable value is missing. Return type:dict

is_affine()

Is the expression affine?

is_concave()

Is the expression concave?

is_constant()

Is the expression constant?

is_convex()

Is the expression convex?

is_dcp()

Checks whether the constraint is DCP.

Returns:True if the constraint is DCP, False otherwise. Return type:bool

is_nonneg()

Is the expression positive?

is_nonpos()

Is the expression negative?

is_zero()

Is the expression all zero?

name()

str : The string representation of the expression.

ndim

int – The number of dimensions in the expression’s shape.

shape

tuple – The expression dimensions.

sign

str – The sign of the expression.

size

int – The number of entries in the expression.

value

NumPy.ndarray or None – The numeric value of the expression.

Leaf

class cvxpy.expressions.leaf.Leaf(shape, value=None, nonneg=False, nonpos=False, complex=False, imag=False, symmetric=False, diag=False, PSD=False, NSD=False, hermitian=False, boolean=False, integer=False, sparsity=None)

Bases: cvxpy.expressions.expression.Expression

A leaf node of an expression tree; i.e., a Variable, Constant, or Parameter.

A leaf may carry attributes that constrain the set values permissible for it. Leafs can have no more than one attribute, with the exception that a leaf may be both nonpos and nonneg or both boolean in some indices and integer in others.

An error is raised if a leaf is assigned a value that contradicts one or more of its attributes. See the project method for a convenient way to project a value onto a leaf’s domain.

Parameters:

• shape (tuple or int) – The leaf dimensions. Either an integer n for a 1D shape, or a tuple where the semantics are the same as NumPy ndarray shapes. Shapes cannot be more than 2D.
• value (numeric type) – A value to assign to the leaf.
• nonneg (bool) – Is the variable constrained to be nonnegative?
• nonpos (bool) – Is the variable constrained to be nonpositive?
• complex (bool) – Is the variable complex valued?
• symmetric (bool) – Is the variable symmetric?
• diag (bool) – Is the variable diagonal?
• PSD (bool) – Is the variable constrained to be positive semidefinite?
• NSD (bool) – Is the variable constrained to be negative semidefinite?
• Hermitian (bool) – Is the variable Hermitian?
• boolean (bool or list of tuple) – Is the variable boolean? True, which constrains the entire Variable to be boolean, False, or a list of indices which should be constrained as boolean, where each index is a tuple of length exactly equal to the length of shape.
• integer (bool or list of tuple) – Is the variable integer? The semantics are the same as the boolean argument.
• sparsity (list of tuplewith) – Fixed sparsity pattern for the variable.

T

Expression – The transpose of the expression.

ndim

int – The number of dimensions in the expression’s shape.

project(val)

Project value onto the attribute set of the leaf.

A sensible idiom is leaf.value = leaf.project(val).

Parameters:val (numeric type) – The value assigned. Returns:The value rounded to the attribute type. Return type:numeric type

project_and_assign(val)

Project and assign a value to the variable.

shape

tuple – The dimensions of the expression.

size

int – The number of entries in the expression.

value

NumPy.ndarray or None – The numeric value of the parameter.

Variable

class cvxpy.expressions.variable.Variable(shape=(), name=None, var_id=None, **kwargs)

Bases: cvxpy.expressions.leaf.Leaf

The optimization variables in a problem.

T

Expression – The transpose of the expression.

name()

str : The name of the variable.

ndim

int – The number of dimensions in the expression’s shape.

project(val)

Project value onto the attribute set of the leaf.

A sensible idiom is leaf.value = leaf.project(val).

Parameters:val (numeric type) – The value assigned. Returns:The value rounded to the attribute type. Return type:numeric type

project_and_assign(val)

Project and assign a value to the variable.

shape

tuple – The dimensions of the expression.

size

int – The number of entries in the expression.

value

NumPy.ndarray or None – The numeric value of the parameter.

Parameter

class cvxpy.expressions.constants.parameter.Parameter(shape=(), name=None, value=None, **kwargs)

Bases: cvxpy.expressions.leaf.Leaf

Parameters in optimization problems.

Parameters are constant expressions whose value may be specified after problem creation. The only way to modify a problem after its creation is through parameters. For example, you might choose to declare the hyper-parameters of a machine learning model to be Parameter objects; more generally, Parameters are useful for computing trade-off curves.

T

Expression – The transpose of the expression.

ndim

int – The number of dimensions in the expression’s shape.

project(val)

Project value onto the attribute set of the leaf.

A sensible idiom is leaf.value = leaf.project(val).

Parameters:val (numeric type) – The value assigned. Returns:The value rounded to the attribute type. Return type:numeric type

project_and_assign(val)

Project and assign a value to the variable.

shape

tuple – The dimensions of the expression.

size

int – The number of entries in the expression.

value

NumPy.ndarray or None – The numeric value of the parameter.

Constant

class cvxpy.expressions.constants.Constant(value)

Bases: cvxpy.expressions.leaf.Leaf

A constant value.

Raw numerical constants (Python primite types, NumPy ndarrays, and NumPy matrices) are implicitly cast to constants via Expression operator overloading. For example, if x is an expression and c is a raw constant, then x + c creates an expression by casting c to a Constant.

T

Expression – The transpose of the expression.

ndim

int – The number of dimensions in the expression’s shape.

shape

Returns the (row, col) dimensions of the expression.

size

int – The number of entries in the expression.

value

NumPy.ndarray or None – The numeric value of the constant.