# Matrix Modeling Method

The COPT Python API provides matrix modeling, supports NumPy multi-dimensional array, a two-dimensional NumPy matrix, SciPy compressed sparse column matrix ( csc_matrix ) and compressed sparse row matrix ( csr_matrix ) operations ( NumPy minimum version requirement is 1.23, Python minimum version requirement is 3.8) and can be combined with ordinary (scalar) variables and constraints. COPT mainly provides the following utilities:

1. Add multi-dimensional variables ( MVar ) and other related operations;

2. Construct multi-dimensional linear expressions ( MLinExpr ), add multi-dimensional linear constraints ( MConstr ) and other related operations;

## Multi-dimensional Variables

MVar constains operations related to multi-dimensional variables. Users can use Model.addMVar() to add a matmulti-dimensionalrix variable MVar of any dimension and shape to the model. In addition to the need of specifying the argument shape (matrix shape), the rest of the arguments are consistent with ordinary variables, including: lb , ub , vtype , nameprefix .

In addition, the MVar multi-dimensional variables can also be sliced, such as: y1 = y[:,0:2]

1. Get multi-dimensional variable related attributes:

• Number of dimensions: MVar.ndim

• The shape of the multi-dimensional variable: MVar.shape

• Number of elements in the multi-dimensional variable: MVar.size

## Multi-dimensional array operations and expressions

### Multi-dimensional Linear Expressions

Multi-dimensional variables and their coefficients (can be ndarray ) form a Multi-dimensional linear expression (MLinExpr), and the supported operations mainly include:

1. Matrix multiplication: A @ x

A = np.array([[1, 0, 1],[0, 0, 1]])
expr1 = A @ x
1. Vector inner product

c = np.array([1, 2, 3])
expr2 = c @ x

Common multi-dimensional quadratic expressions and their corresponding mathematical forms are as follows:

• x @ Q @ x: $$x^TQx$$

• x @ x: $$x^Tx$$

• x @ Q @ x + c @ x + b: $$x^TQx+c^Tx+b$$

### Other multi-dimensional array operations

1. Combine with regular linear variables, regular linear expressions, and constants:

c = np.array([1, 2, 3])
Q = np.full((3, 3), 1)
expr3 = 2 * x @ Q @ x + c @ x + 2 * y + 1
1. Self-increment/self-subtraction/self-multiplication operations:

B = np.array([[1, 0, 1], [0, 1, 1]])

Notes

• When we directly print the multi-dimensional expression with print(MLinExpr)/print(MQuadExpr) , the shape of the expression will be output at the same time. When shape=() , it means that the expression is a scalar (single linear/quadratic expression), corresponding to ndim=0 , size=1 . The same is true for multi-dimensional variables MVar ;

• When performing matrix multiplication (A@x), the matrix multiplication algorithm needs to be satisfied, and the number of columns of A and the number of rows of X need to be the same;

• COPT supports the combination of MLinExpr and LinExpr , but it should be noted that the MLinExpr needs shape=() at this time, and the final returned expression is MLinExpr with shape=() .

## Matrix Constraints

### Matrix linear Constraints

COPT supports two ways of adding multi-dimensional linear constraints, and the format provided by the function arguments is different:

1. Model.addMConstr() that specifically adds multi-dimensional linear constraints, the arguments that can be specified are:

• A : coefficient matrix for linear constraints

• x : decision variables ( MVar )

• sense : type of linear constraint, the possible values are: 'L' (<=), 'G' (>=), 'E' (=)

• b : right-hand-side of linear constraints (vector with dimensions equal to the number of rows of matrix A)

• name : name prefix for linear constraints

A = np.array([[1, 2, 3], [3, 2, 1]])
b = np.array([2, 5])
mconstrs = model.addMConstr(A, x, 'L', b, nameprefix='c')
obj = np.array([1, 2, 1])
model.setObjective(obj @ x, COPT.MINIMIZE)
1. Matrix linear constraints can be regarded as a set of linear constraints, so Model.addConstrs() can also add multi-dimensional linear constraints:

A = np.array([[1, 2, 3], [3, 2, 1]])
b = np.array([2, 5])
mconstrs = model.addConstrs(A @ x <= b, nameprefix='c')
obj = np.array([1, 2, 1])
model.setObjective(obj @ x, COPT.MINIMIZE)

COPT supports two ways of constructing multi-dimensional quadratic constraints, and the format provided by the function arguments is different:

• Q : quadratic coefficient matrix

• c : vector of linear term coefficients, or None if there is no linear term

• sense : type of quadratic constraint, the possible values are: 'L' (<=), 'G' (>=), 'E' (=)

• rhs : right-hand-side of quadratic constraints

• xQ_L : the left-hand variable of the quadratic coefficient matrix Q (vector whose length is consistent with the number of rows of the matrix Q )

• xQ_R : right-hand variable of the quadratic coefficient matrix Q (vector whose length is consistent with the number of columns of the matrix Q )

• xc : the variables for the linear term, or None if there is no linear term

• name : name prefix for quadratic constraints

Q = np.diag([3, 2, 1])
c1 = model.addMQConstr(Q, None, 'E', 1.0, x, y)

• lhs : multi-dimensional quadratic expression

• sense : constraint type

• rhs : right-hand-side of quadratic constraints

Q = np.diag([3, 2, 1])

## Objective function composed of multi-dimensional variables

COPT supports setting linear and quadratic objective functions, and provides two ways to set objective functions. The format of function arguments is different:

1. Model.setMObjective() that specifically sets the objective function composed of multi-dimensional variables, the arguments that can be specified are:

• Q : quadratic coefficient matrix, or None if the objective function is linear

• c : vector of linear term coefficients, or None if there is no linear term

• constant : the constant term of the objective function

• xQ_L: the left-hand variable of the quadratic term coefficient matrix Q (vector whose length is consistent with the number of rows of the matrix Q), or None if the objective function is linear

• xQ_R: the right-hand variable of the quadratic coefficient matrix Q (vector, whose length is consistent with the number of columns in the matrix Q), or None if the objective function is linear

• xc: the variable for the linear term, or None if there is no linear term

• sense: direction of optimization, possible values are: COPT.MINIMIZE or COPT.MAXIMIZE

2. Model.setObjective() that directly gives the expression of the objective function

• expr: Objective function expression, which can be linear or quadratic

• sense: optimization direction, possible values are: COPT.MINIMIZE or COPT.MINIMIZE