14.3.12 Class Domain

mosek.fusion.Domain

The Domain class defines a set of static method for creating various variable and constraint domains. A Domain object specifies a subset of ${\mathbb{R}}^n$, which can be used to define the feasible domain of variables and expressions.

For further details on the use of these, see Model.variable and Model.constraint.

Static members:

Domain.axis – Set the dimension along which the cones are created.

Domain.binary – Creates a domain of binary variables.

Domain.equalsTo (==) – Defines the domain consisting of a fixed point.

Domain.greaterThan (>=) – Defines the domain specified by a lower bound in each dimension.

Domain.inDExpCone – Defines the dual exponential cone.

Domain.inDGeoMeanCone – Defines the domain of dual geometric mean cones.

Domain.inDPowerCone – Defines the dual power cone.

Domain.inPExpCone – Defines the primal exponential cone.

Domain.inPGeoMeanCone – Defines the domain of primal geometric mean cones.

Domain.inPPowerCone – Defines the primal power cone.

Domain.inPSDCone – Creates a domain of Positive Semidefinite matrices.

Domain.inQCone – Defines the domain of quadratic cones.

Domain.inRange – Creates a domain specified by a range in each dimension.

Domain.inRotatedQCone – Defines the domain of rotated quadratic cones.

Domain.inSVecPSDCone – Creates a domain of vectorized Positive Semidefinite matrices.

Domain.integral – Creates a domain of integral variables.

Domain.isTrilPSD – Creates a domain of Positive Semidefinite matrices.

Domain.lessThan (<=) – Defines the domain specified by an upper bound in each dimension.

Domain.sparse – Use a sparse representation.

Domain.unbounded – Creates a domain in which variables are unbounded.

Domain.axis

Domain.axis(ConeDomain c, int a) -> ConeDomain

Set the dimension along which the cones are created. If this conic domain is used for a variable or expression of dimension $d$, then the conic constraint will be applicable to all vectors obtained by fixing the coordinates other than $a$-th and moving along the $a$-th coordinate. If $d=2$ this can be used to define the conditions “every row of the matrix is in a cone” and “every column of a matrix is in a cone”.

The default is the last dimension $a=d-1$.

Parameters:

• c (ConeDomain) – A conic domain.

• a (int) – The axis.

Return:

(ConeDomain)

Domain.binary

Domain.binary(int n) -> RangeDomain
Domain.binary(int m, int n) -> RangeDomain
Domain.binary(int[] dims) -> RangeDomain
Domain.binary() -> RangeDomain

Create a domain of binary variables. A binary domain can only be used when creating variables, but is not allowed in a constraint. Another way of restricting variables to be integers is the method Variable.makeInteger.

Parameters:

• n (int) – Dimension size.

• m (int) – Dimension size.

• dims (int[]) – A list of dimension sizes.

Return:

(RangeDomain)

Domain.equalsTo (==)

Domain.equalsTo(float b) -> LinearDomain
Domain.equalsTo(float b, int n) -> LinearDomain
Domain.equalsTo(float b, int m, int n) -> LinearDomain
Domain.equalsTo(float b, int[] dims) -> LinearDomain
Domain.equalsTo(float[] a1) -> LinearDomain
Domain.equalsTo(float[][] a2) -> LinearDomain
Domain.equalsTo(float[] a1, int[] dims) -> LinearDomain
Domain.equalsTo(Matrix mx) -> LinearDomain

Defines the domain consisting of a fixed point.

The Python operator/property == can also be used for this purpose. See Sec. 14.2 (Pythonic extensions) for details.

Parameters:

• b (float) – A single value. This is scalable: it means that each element in the variable or constraint is fixed to $b$.

• n (int) – Dimension size.

• m (int) – Dimension size.

• dims (int[]) – A list of dimension sizes.

• a1 (float[]) – A one-dimensional array of bounds. The shape must match the variable or constraint with which it is used.

• a2 (float[][]) – A two-dimensional array of bounds. The shape must match the variable or constraint with which it is used.

• mx (Matrix) – A matrix of bound values. The shape must match the variable or constraint with which it is used.

Return:

(LinearDomain)

Domain.greaterThan (>=)

Domain.greaterThan(float b) -> LinearDomain
Domain.greaterThan(float b, int n) -> LinearDomain
Domain.greaterThan(float b, int m, int n) -> LinearDomain
Domain.greaterThan(float b, int[] dims) -> LinearDomain
Domain.greaterThan(float[] a1) -> LinearDomain
Domain.greaterThan(float[][] a2) -> LinearDomain
Domain.greaterThan(float[] a1, int[] dims) -> LinearDomain
Domain.greaterThan(Matrix mx) -> LinearDomain

Defines the domain specified by a lower bound in each dimension.

The Python operator/property >= can also be used for this purpose. See Sec. 14.2 (Pythonic extensions) for details.

Parameters:

• b (float) – A single value. This is scalable: it means that each element in the variable or constraint is greater than or equal to $b$.

• n (int) – Dimension size.

• m (int) – Dimension size.

• dims (int[]) – A list of dimension sizes.

• a1 (float[]) – A one-dimensional array of bounds. The shape must match the variable or constraint with which it is used.

• a2 (float[][]) – A two-dimensional array of bounds. The shape must match the variable or constraint with which it is used.

• mx (Matrix) – A matrix of bound values. The shape must match the variable or constraint with which it is used.

Return:

(LinearDomain)

Domain.inDExpCone

Domain.inDExpCone() -> ConeDomain
Domain.inDExpCone(int m) -> ConeDomain
Domain.inDExpCone(int[] dims) -> ConeDomain

Defines the domain of dual exponential cones:

$$\{x\in {\mathbb{R}}^3:x_1\ge -x_3{e}^{-1}{e}^{x_2/x_3},x_1>0,x_3<0\}$$

The conic domain scales as follows. If a variable or expression constrained to an exponential cone is not a single vector but a $d$-dimensional variable then the conic domain is applicable to all vectors obtained by fixing the first $d-1$ coordinates and moving along the last coordinate. If $d=2$ it means that each row of a matrix must belong to a cone. See also Domain.axis.

If $m$ was given the domain is a product of $m$ such cones.

Parameters:

• m (int) – The number of cones (default 1).

• dims (int[]) – Shape of the domain.

Return:

(ConeDomain)

Domain.inDGeoMeanCone

Domain.inDGeoMeanCone() -> ConeDomain
Domain.inDGeoMeanCone(int n) -> ConeDomain
Domain.inDGeoMeanCone(int m, int n) -> ConeDomain
Domain.inDGeoMeanCone(int[] dims) -> ConeDomain

Defines the domain of dual geometric mean cones:

$$\{x\in {\mathbb{R}}^n:(n-1){\left(\prod _{i=1}^{n-1}{x}_i\right)}^{1/(n-1)}\ge |x_n|,x_1,\dots ,x_{n-1}\ge 0\}$$

The conic domain scales as follows. If a variable or expression constrained to a cone is not a single vector but a $d$-dimensional variable then the conic domain is applicable to all vectors obtained by fixing the first $d-1$ coordinates and moving along the last coordinate. If $d=2$ it means that each row of a matrix must belong to a cone. See also Domain.axis.

If $m$ was given the domain is a product of $m$ such cones.

Parameters:

• n (int) – The size of each cone; at least 2.

• m (int) – The number of cones (default 1).

• dims (int[]) – Shape of the domain.

Return:

(ConeDomain)

Domain.inDPowerCone

Domain.inDPowerCone(float alpha) -> ConeDomain
Domain.inDPowerCone(float alpha, int m) -> ConeDomain
Domain.inDPowerCone(float alpha, int[] dims) -> ConeDomain
Domain.inDPowerCone(float[] alphas) -> ConeDomain
Domain.inDPowerCone(float[] alphas, int m) -> ConeDomain
Domain.inDPowerCone(float[] alphas, int[] dims) -> ConeDomain

Defines the domain of dual power cones. For a single double argument alpha it defines the set

$$\{x\in {\mathbb{R}}^n:{\left(\frac{x_1}{\alpha }\right)}^{\alpha }{\left(\frac{x_2}{1-\alpha }\right)}^{1-\alpha }\ge \sqrt{\sum _{i=3}^n{x}_i^2},x_1,x_2\ge 0\}.$$

For an array alphas of length $n_l$, consisting of weights for the cone, it defines the set

$$\{x\in {\mathbb{R}}^n:\prod _{i=1}^{n_l}{\left(\frac{x_i}{{\beta }_i}\right)}^{{\beta }_i}\ge \sqrt{x_{n_l+1}^2+\cdots +x_n^2},x_1,\dots ,x_{n_l}\ge 0\}.$$

where ${\beta }_i$ are the weights normalized to add up to $1$, ie. ${\beta }_i={\alpha }_i/(\sum _j{\alpha }_j)$ for $i=1,\dots ,n_l$.

The conic domain scales as follows. If a variable or expression constrained to a power cone is not a single vector but a $d$-dimensional variable then the conic domain is applicable to all vectors obtained by fixing the first $d-1$ coordinates and moving along the last coordinate. If $d=2$ it means that each row of a matrix must belong to a cone. See also Domain.axis.

If $m$ was given the domain is a product of $m$ such cones.

Parameters:

• alpha (float) – The exponent of the power cone. Must be between 0 and 1.

• m (int) – The number of cones (default 1).

• dims (int[]) – Shape of the domain.

• alphas (float[]) – The weights of the power cone. Must be positive.

Return:

(ConeDomain)

Domain.inPExpCone

Domain.inPExpCone() -> ConeDomain
Domain.inPExpCone(int m) -> ConeDomain
Domain.inPExpCone(int[] dims) -> ConeDomain

Defines the domain of primal exponential cones:

$$\{x\in {\mathbb{R}}^3:x_1\ge x_2{e}^{x_3/x_2},x_1,x_2>0\}$$

The conic domain scales as follows. If a variable or expression constrained to an exponential cone is not a single vector but a $d$-dimensional variable then the conic domain is applicable to all vectors obtained by fixing the first $d-1$ coordinates and moving along the last coordinate. If $d=2$ it means that each row of a matrix must belong to a cone. See also Domain.axis.

If $m$ was given the domain is a product of $m$ such cones.

Parameters:

• m (int) – The number of cones (default 1).

• dims (int[]) – Shape of the domain.

Return:

(ConeDomain)

Domain.inPGeoMeanCone

Domain.inPGeoMeanCone() -> ConeDomain
Domain.inPGeoMeanCone(int n) -> ConeDomain
Domain.inPGeoMeanCone(int m, int n) -> ConeDomain
Domain.inPGeoMeanCone(int[] dims) -> ConeDomain

Defines the domain of primal geometric mean cones:

$$\{x\in {\mathbb{R}}^n:{\left(\prod _{i=1}^{n-1}{x}_i\right)}^{1/(n-1)}\ge |x_n|,x_1\dots ,x_{n-1}\ge 0\}$$

The conic domain scales as follows. If a variable or expression constrained to a cone is not a single vector but a $d$-dimensional variable then the conic domain is applicable to all vectors obtained by fixing the first $d-1$ coordinates and moving along the last coordinate. If $d=2$ it means that each row of a matrix must belong to a cone. See also Domain.axis.

If $m$ was given the domain is a product of $m$ such cones.

Parameters:

• n (int) – The size of each cone; at least 2.

• m (int) – The number of cones (default 1).

• dims (int[]) – Shape of the domain.

Return:

(ConeDomain)

Domain.inPPowerCone

Domain.inPPowerCone(float alpha) -> ConeDomain
Domain.inPPowerCone(float alpha, int m) -> ConeDomain
Domain.inPPowerCone(float alpha, int[] dims) -> ConeDomain
Domain.inPPowerCone(float[] alphas) -> ConeDomain
Domain.inPPowerCone(float[] alphas, int m) -> ConeDomain
Domain.inPPowerCone(float[] alphas, int[] dims) -> ConeDomain

Defines the domain of primal power cones. For a single double argument alpha it defines the set

$$\{x\in {\mathbb{R}}^n:x_1^{\alpha }{x}_2^{1-\alpha }\ge \sqrt{\sum _{i=3}^n{x}_i^2},x_1,x_2\ge 0\}.$$

For an array alphas of length $n_l$, consisting of weights for the cone, it defines the set

$$\{x\in {\mathbb{R}}^n:\prod _{i=1}^{n_l}{x}_i^{{\beta }_i}\ge \sqrt{x_{n_l+1}^2+\cdots +x_n^2},x_1,\dots ,x_{n_l}\ge 0\}.$$

where ${\beta }_i$ are the weights normalized to add up to $1$, ie. ${\beta }_i={\alpha }_i/(\sum _j{\alpha }_j)$ for $i=1,\dots ,n_l$.

The conic domain scales as follows. If a variable or expression constrained to a power cone is not a single vector but a $d$-dimensional variable then the conic domain is applicable to all vectors obtained by fixing the first $d-1$ coordinates and moving along the last coordinate. If $d=2$ it means that each row of a matrix must belong to a cone. See also Domain.axis.

If $m$ was given the domain is a product of $m$ such cones.

Parameters:

• alpha (float) – The exponent of the power cone. Must be between 0 and 1.

• m (int) – The number of cones (default 1).

• dims (int[]) – Shape of the domain.

• alphas (float[]) – The weights of the power cone. Must be positive.

Return:

(ConeDomain)

Domain.inPSDCone

Domain.inPSDCone() -> PSDDomain
Domain.inPSDCone(int n) -> PSDDomain
Domain.inPSDCone(int n, int m) -> PSDDomain

When used to create a new variable in Model.variable it defines a domain of symmetric positive semidefinite matrices, that is

$${\mathcal{S}}_{+}^n=\{X\in {\mathbb{R}}^{n\times n}:X=X^T,y^{T}Xy\ge 0,\text{for all}y\}.$$

The shape of the result is $n\times n$. If $m$ was given the domain is a product of $m$ such cones, that is of shape $m\times n\times n$.

When used to impose a constraint in Model.constraint it defines a domain

$$\{X\in {\mathbb{R}}^{n\times n}:\frac{1}{2}(X+X^T)\in {\mathcal{S}}_{+}^n\}.$$

i.e. a positive semidefinite matrix without the symmetry assumption.

Parameters:

• n (int) – Dimension of the PSD matrix.

• m (int) – Number of matrices (default 1).

Return:

(PSDDomain)

Domain.inQCone

Domain.inQCone() -> ConeDomain
Domain.inQCone(int n) -> ConeDomain
Domain.inQCone(int m, int n) -> ConeDomain
Domain.inQCone(int[] dims) -> ConeDomain

Defines the domain of quadratic cones:

$$\{x\in {\mathbb{R}}^n:x_1^2\ge \sum _{i=2}^n{x}_i^2,x_1\ge 0\}$$

The conic domain scales as follows. If a variable or expression constrained to a quadratic cone is not a single vector but a $d$-dimensional variable then the conic domain is applicable to all vectors obtained by fixing the first $d-1$ coordinates and moving along the last coordinate. If $d=2$ it means that each row of a matrix must belong to a cone. See also Domain.axis.

If $m$ was given the domain is a product of $m$ such cones.

Parameters:

• n (int) – The size of each cone; at least 2.

• m (int) – The number of cones (default 1).

• dims (int[]) – Shape of the domain.

Return:

(ConeDomain)

Domain.inRange

Domain.inRange(float lb, float ub) -> RangeDomain
Domain.inRange(float lb, float[] uba) -> RangeDomain
Domain.inRange(float[] lba, float ub) -> RangeDomain
Domain.inRange(float[] lba, float[] uba) -> RangeDomain
Domain.inRange(float lb, float ub, int[] dims) -> RangeDomain
Domain.inRange(float lb, float[] uba, int[] dims) -> RangeDomain
Domain.inRange(float[] lba, float ub, int[] dims) -> RangeDomain
Domain.inRange(float[] lba, float[] uba, int[] dims) -> RangeDomain
Domain.inRange(float[][] lba, float[][] uba) -> RangeDomain
Domain.inRange(Matrix lbm, Matrix ubm) -> RangeDomain

Creates a domain specified by a range in each dimension.

Parameters:

• lb (float) – The lower bound as a common scalar value.

• ub (float) – The upper bound as a common scalar value.

• uba (float[]) – The upper bounds as an array.

• uba (float[][]) – The upper bounds as an array.

• lba (float[]) – The lower bounds as an array.

• lba (float[][]) – The lower bounds as an array.

• dims (int[]) – A list of dimension sizes.

• lbm (Matrix) – The lower bounds as a Matrix object.

• ubm (Matrix) – The upper bounds as a Matrix object.

Return:

(RangeDomain)

Domain.inRotatedQCone

Domain.inRotatedQCone() -> ConeDomain
Domain.inRotatedQCone(int n) -> ConeDomain
Domain.inRotatedQCone(int m, int n) -> ConeDomain
Domain.inRotatedQCone(int[] dims) -> ConeDomain

Defines the domain of rotated quadratic cones:

$$\{x\in {\mathbb{R}}^n:2x_1{x}_2\ge \sum _{i=3}^n{x}_i^2,x_1,x_2\ge 0\}$$

The conic domain scales as follows. If a variable or expression constrained to a quadratic cone is not a single vector but a $d$-dimensional variable then the conic domain is applicable to all vectors obtained by fixing the first $d-1$ coordinates and moving along the last coordinate. If $d=2$ it means that each row of a matrix must belong to a cone. See also Domain.axis.

If $m$ was given the domain is a product of $m$ such cones.

Parameters:

• n (int) – The size of each cone; at least 3.

• m (int) – The number of cones (default 1).

• dims (int[]) – Shape of the domain.

Return:

(ConeDomain)

Domain.inSVecPSDCone

Domain.inSVecPSDCone() -> ConeDomain
Domain.inSVecPSDCone(int n) -> ConeDomain
Domain.inSVecPSDCone(int d1, int d2) -> ConeDomain
Domain.inSVecPSDCone(int[] dims) -> ConeDomain

Creates a domain of vectorized Positive Semidefinite matrices:

$$\{(x_1,\dots ,x_{d(d+1)/2})\in {\mathbb{R}}^n:\mathrm{sMat}(x)\in {\mathcal{S}}_{+}^d\}=\{\mathrm{sVec}(X):X\in {\mathcal{S}}_{+}^d\},$$

where

$$\mathrm{sVec}(X)=(X_{11},\sqrt{2}{X}_{21},\dots ,\sqrt{2}{X}_{d1},X_{22},\sqrt{2}{X}_{32},\dots ,X_{dd}),$$

and

$$\begin{array}{r}\mathrm{sMat}(x)=\left[\begin{array}{cccc}{x}_1& x_2/\sqrt{2}& \cdots & x_d/\sqrt{2}\\ x_2/\sqrt{2}& x_{d+1}& \cdots & x_{2d-1}/\sqrt{2}\\ \cdots & \cdots & \cdots & \cdots \\ x_d/\sqrt{2}& x_{2d-1}/\sqrt{2}& \cdots & x_{d(d+1)/2}\end{array}\right].\end{array}$$

In other words, the domain consists of vectorizations of the lower-triangular part of a positive semidefinite matrix, with the non-diagonal elements additionally rescaled.

Parameters:

• n (int) – Length of the vectorization - this must be of the form $d\ast (d+1)/2$ for some positive integer $d$.

• d1 (int) – Size of first dimension of the domain.

• d2 (int) – Size of second dimension of the domain.

• dims (int[]) – Shape of the domain.

Return:

(ConeDomain)

Domain.integral

Domain.integral(ConeDomain c) -> ConeDomain
Domain.integral(LinearDomain ld) -> LinearDomain
Domain.integral(RangeDomain rd) -> RangeDomain

Modify a given domain restricting its elements to be integral. An integral domain can only be used when creating variables, but is not allowed in a constraint. Another way of restricting variables to be integers is the method Variable.makeInteger.

Parameters:

• c (ConeDomain) – A conic domain.

• ld (LinearDomain) – A linear domain.

• rd (RangeDomain) – A ranged domain.

Return:

• (ConeDomain)

• (LinearDomain)

• (RangeDomain)

Domain.isTrilPSD

Domain.isTrilPSD() -> PSDDomain
Domain.isTrilPSD(int n) -> PSDDomain
Domain.isTrilPSD(int n, int m) -> PSDDomain

Creates an object representing a cone of the form

$$\{X\in {\mathbb{R}}^{n\times n}:\text{tril}(X)\in {\mathcal{S}}_{+}^n\}.$$

i.e. the lower triangular part of $X$ defines the symmetric matrix that is positive semidefinite. The shape of the result is $n\times n$. If $m$ was given the domain is a product of $m$ such cones, that is of shape $m\times n\times n$.

Parameters:

• n (int) – Dimension of the PSD matrix.

• m (int) – Number of matrices (default 1).

Return:

(PSDDomain)

Domain.lessThan (<=)

Domain.lessThan(float b) -> LinearDomain
Domain.lessThan(float b, int n) -> LinearDomain
Domain.lessThan(float b, int m, int n) -> LinearDomain
Domain.lessThan(float b, int[] dims) -> LinearDomain
Domain.lessThan(float[] a1) -> LinearDomain
Domain.lessThan(float[][] a2) -> LinearDomain
Domain.lessThan(float[] a1, int[] dims) -> LinearDomain
Domain.lessThan(Matrix mx) -> LinearDomain

Defines the domain specified by an upper bound in each dimension.

The Python operator/property <= can also be used for this purpose. See Sec. 14.2 (Pythonic extensions) for details.

Parameters:

• b (float) – A single value. This is scalable: it means that each element in the variable or constraint is less than or equal to $b$.

• n (int) – Dimension size.

• m (int) – Dimension size.

• dims (int[]) – A list of dimension sizes.

• a1 (float[]) – A one-dimensional array of bounds. The shape must match the variable or constraint with which it is used.

• a2 (float[][]) – A two-dimensional array of bounds. The shape must match the variable or constraint with which it is used.

• mx (Matrix) – A matrix of bound values. The shape must match the variable or constraint with which it is used.

Return:

(LinearDomain)

Domain.sparse

Domain.sparse(LinearDomain ld, int[] sparsity) -> LinearDomain
Domain.sparse(LinearDomain ld, int[][] sparsity) -> LinearDomain
Domain.sparse(RangeDomain rd, int[] sparsity) -> RangeDomain
Domain.sparse(RangeDomain rd, int[][] sparsity) -> RangeDomain

Given a linear domain, this method explicitly suggest to Fusion that a sparse representation is helpful.

Parameters:

• ld (LinearDomain) – The linear sparse domain.

• sparsity (int[]) – Sparsity pattern.

• sparsity (int[][]) – Sparsity pattern.

• rd (RangeDomain) – The ranged sparse domain.

Return:

• (LinearDomain)

• (RangeDomain)

Domain.unbounded

Domain.unbounded() -> LinearDomain
Domain.unbounded(int n) -> LinearDomain
Domain.unbounded(int m, int n) -> LinearDomain
Domain.unbounded(int[] dims) -> LinearDomain

Creates a domain in which variables are unbounded.

Parameters:

• n (int) – Dimension size.

• m (int) – Dimension size.

• dims (int[]) – A list of dimension sizes.

Return:

(LinearDomain)