6 Conic Modeling¶
6.1 The model¶
A model built using Fusion is always a conic optimization problem and it is convex by definition. These problems can be succinctly characterized as
where \(\K\) is a product of the following basic types of cones:
 linear: \(\real^n\), \(\{ x \in \R^n ~:~ x \leq u\}\), \(\{ x \in \R^n ~:~ l\leq x \}\), \(\{ x \in \R^n ~:~ l\leq x \leq u\}\), a point,
 quadratic: \(\Q^n = \{x\in\real^n~:~x_1\geq\sqrt{x_2^2+\cdots+x_n^2}\}\),
 rotated quadratic: \(\Q_r^n = \{x\in\real^n~:~2x_1x_2\geq x_3^2+\cdots+x_n^2,\ x_1,x_2\geq 0\}\),
 semidefinite:: \(\PSD^n=\{X\in\real^{n\times n}~:~ X\ \textrm{is symmmetric positive semidefinite}\}\).
The main thing about a Fusion model is that it can be specified in a convenient way without explicitly constructing the representation (1). Instead the user has access to variables which are used to construct linear operators that appear in constraints. The cone types described above are the domains of those constraints. A Fusion model can potentially contain many different building blocks of that kind. To facilitate manipulations with a large number of variables Fusion defines various logical views of parts of the model.
This section briefly summarizes the constructions and techniques available in Fusion. See Section 7 for a basic tutorial and Section 10 for more advanced case studies. This section is only an introduction: detailed specification of the methods and classes mentioned here can be found in the API reference.
A Fusion model is represented by the class Model
and created by a simple construction
M = Model();
The model object is the user’s interface to the optimization problem, used in particular for
 formulating the problem by defining variables, constraints and objective,
 solving the problem and retrieving the solution status and solutions,
 interacting with the solver: setting up parameters, registering for callbacks, performing I/O, obtaining detailed information from the optimizer etc.
 memory management.
Almost all elements of the model: variables, constraints and the model itself can be constructed with or without names. If used, the names for each type of object must be unique. Choosing a good naming convention can make the problem more readable when dumped to a file. Most Fusion components also support some degree of pretty printing (toString
method).
6.2 Variables¶
Continuous variables can be scalars, vectors or higherdimensional arrays. The are added to the model with the method Model.variable
which returns a representing object of type Variable
. The shape of a variable (number of dimensions and length in each dimension) has to be specified at creation. Optionally a variable may be created in a restricted domain (by default variables are unbounded, that is in \(\real\)). For instance, to declare a variable \(x\in\real_+^n\) we could write
x = M.variable('x', n, Domain.greaterThan(0.));
A multidimensional variable is declared by specifying an array with all dimension sizes. Here is an \(n\times n\) variable:
x = M.variable( [n,n], Domain.unbounded() );
The specification of dimensions can also be part of the domain, as in this declaration of a symmetric positive semidefinite variable of dimension \(n\):
v = M.variable(Domain.inPSDCone(n));
Integer variables are specified with an additional domain modifier. To add an integer variable \(z\in [1,10]\) we write
z= M.variable('z', Domain.integral(Domain.inRange(1.,10.)) );
The function Domain.binary
is a shorthand for binary variables often appearing in combinatorial problems:
y= M.variable('y', Domain.binary());
Integrality requirement can be switched on and off using the methods Variable.makeInteger
and Variable.makeContinuous
.
The Variable
object provides the primal (Variable.level
) and dual (Variable.dual
) solution values of the variable after optimization, and it enters in the construction of linear expressions involving the variable.
6.3 Linear algebra¶
Linear expressions are constructed combining variables and matrices by linear operators. The result is an object that represents the linear expression itself. Fusion only allows for those combinations of operators and arguments that yield linear functions of the variables. Expressions have shapes and dimensions in the same fashion as variables. For instance, if \(x\in \real^n\) and \(A \in \real^{m\times n}\), then \(Ax\) is a vector expression of length \(m\). Note, however, that the internal size of \(Ax\) is \(mn\), because each entry is a linear combination for which \(m\) coefficients have to be stored.
Expressions are concrete implementations of the virtual interface Expression
. In typical situations, however, all operations on expressions can be performed using the static methods and factory methods of the class Expr
.
Method  Description 

Expr.add 
Elementwise addition of two matrices 
Expr.sub 
Elementwise subtraction of two matrices 
Expr.mul 
Matrix or matrixscalar multiplication 
Expr.neg 
Sign inversion 
Expr.outer 
Vector outerproduct 
Expr.dot 
Dot product 
Expr.sum 
Sum over a given dimension 
Expr.mulDiag 
Sum over the diagonal of a matrix which is the result of a matrix multiplication 
Expr.constTerm 
Return a constant term 
Operations on expressions must adhere to the rules of matrix algebra regarding dimensions; otherwise a DimensionError
exception will be thrown.
Expression can be composed, nested and used as building blocks in new expressions. For instance \(Ax + By\) can be implemented as:
Expr.add( Expr.mul(A,x), Expr.mul(B,y) );
For operations involving multiple variables and expressions the users should consider listbased methods. For instance, a clean way to write \(x+y+z+w\) would be:
Expr.add( [x, y, z, w] );
Note that a single variable (object of class Variable
) can also be used as an expression. Once constructed, expressions are immutable.
6.4 Constraints and objective¶
Constraints are declared within an optimization model using the method Model.constraint
. Every constraint in Fusion has the form
Expression belongs to a Domain . 
Objects of type Domain
correspond roughly to the types of convex cones \(\mathcal{K}\) mentioned at the beginning of this section. For instance, the following set of linear constraints
could be declared as
A = [ 1.0, 2.0, 0.0;
0.0, 1.0, 1.0;
1.0, 0.0, 0.0 ];
x = M.variable('x',3,Domain.unbounded());
c = M.constraint( Expr.mul(A,x), Domain.equalsTo(0.0));
Note that the scalar domain Domain.equalsTo
consisting of a single point \(0\) scales up to the dimension of the expression and applies to all its elements. This allows many constraints to be comfortably expressed in a vectorized form. See also Section 6.7.
The Constraint
object provides the dual (Constraint.dual
) value of the constraint after optimization and the primal value of the constraint expression (Constraint.level
).
The typical domains used to specify constraints are listed below. Note that they can also be used directly at variable creation, whenever that makes sense.
Type  Domain  

Linear  equality  Domain.equalsTo 
inequality \(\leq\)  Domain.lessThan 

inequality \(\geq\)  Domain.greaterThan 

twosided bound  Domain.inRange 

Conic Quadratic  quadratic cone  Domain.inQCone 
rotated quadratic cone  Domain.inRotatedQCone 

Semidefinite  PSD matrix  Domain.inPSDCone 
Integral  Integers in domain D  Domain.integral (D) 
\(\{0,1\}\)  Domain.binary 
Having discussed variables and constraints we can finish by defining the optimization objective with Model.objective
. The objective function is a scalar expression and the objective sense is specified by the enumeration ObjectiveSense
as either minimize or maximize. The typical linear objective function \(c^T x\) can be declared as
M.objective( ObjectiveSense.Minimize, Expr.mul(c,x) );
6.5 Matrices¶
At some point it becomes necessary to specify linear expressions such as \(Ax\) where \(A\) is a (large) constant data matrix. Such coefficient matrices can be represented in dense or sparse format. Dense matrices can always be represented using the standard data structures for arrays and twodimensional arrays built into the language. Alternatively, or when sparsity can be exploited, matrices can be constructed as objects of the class Matrix
. This can have some advantages: a more generic code that can be ported across platforms and can be used with both dense and sparse matrices without modifications.
Dense matrices are constructed with a variant of the static factory method Matrix.dense
. The values of all entries must be specified all at once and the resulting matrix is immutable. For example the matrix
can be defined with:
A= [ [1.,2.,3.,4.], [5.,6.,7.,8.] ];
Ad= Matrix.dense(A);
or from a flattened representation:
A= [ 1,2,3,4,5,6,7,8 ];
Af= Matrix.dense(2, 4,A);
Sparse matrices are constructed with a variant of the static factory method Matrix.sparse
. This is both speed and memoryefficient when the matrix has few nonzero entries. A matrix \(A\) in sparse format is given by a list of triples \((i, j, v)\), each defining one entry: \(A_{i,j}=v\). The order does not matter. The entries not in the list are assumed to be \(0\). For example, take the matrix
Assuming we number rows and columns from \(0\), the corresponding list of triplets is:
The Fusion definition would be:
rows = [ 1, 1, 2, 2 ];
cols = [ 1, 4, 2, 4 ];
values= [ 1.0, 2.0, 3.0, 4.0 ];
m = Matrix.sparse(4, 4, rows, cols, values);
The Matrix
class provides more standard constructions such as the identity matrix, a constant value matrix, block diagonal matrices etc.
6.6 Stacking and views¶
Fusion provides a way to construct logical views of parts of existing expressions or combinations of existing expressions. They are still represented by objects of type Variable
or Expression
that refer to the original ones. This can be useful in some scenarios:
 retrieving only the values of a few variables, and ignoring the remaining auxiliary ones,
 stacking vectors or matrices to perform various matrix operations,
 bundling a number of similar constraints into one (see Vectorization),
 adding constraints between parts of the same variable, etc.
All these operations do not require new variables or expressions, but just lightweight logical views. In what follows we will concentrate on expressions; the same techniques are available for variables. These techniques will be familiar to the users of numerical tools such as Matlab or NumPy.
Picking and slicing
Expression.pick
picks a subset of entries from a variable or expression. Special cases of picking are Expression.index
, which picks just one scalar entry and Expression.slice
which picks a slice, that is restricts each dimension to a subinterval. Slicing is a frequently used operation.
Both displayed regions are slices of the twodimensional \(4\times 4\) expression, which can be selected as follows:
A1 = Ax.slice([1,1],[3,3]);
A2 = Ax.index([2,2]);
Reshaping
Expressions can be reshaped creating a view with the same number of coordinates arranged in a different way. A particular example of this operation if flattening, which converts any multidimensional expression into a onedimensional vector.
Stacking
Stacking refers to the concatenation of expressions to form a new larger one. For example, the next figure depicts the vertical stacking of two vectors of shape \(1\times 3\) resulting in a matrix of shape \(2\times 3\).
c = Expr.vstack([a, b]);
Vertical stacking (Expr.vstack
) of expressions of shapes \(d_1\times d_2\) and \(d_1'\times d_2\) has shape \((d_1+d_1')\times d_2\). Similarly, horizontal stacking (Expr.hstack
) of expressions of shapes \(d_1\times d_2\) and \(d_1\times d_2'\) has shape \(d_1\times (d_2+d_2')\). Fusion supports also more general versions of stacking for multidimensional variables, as described in Expr.stack
. A special case of stacking is repetition (Expr.repeat
), equivalent to stacking copies of the same expression.
6.7 Vectorization¶
Using Fusion one can compactly express sequences of similar constraints. For example, if we want to express
we can think of \(x_i\in\real^m, b_i\in\real^k\) as the columns of two matrices \(X=[x_1,\ldots,x_n]\in\real^{m\times n}\), \(B=[b_1,\ldots,b_n]\in\real^{k\times n}\), and write simply
M.constraint(Expr.sub(Expr.mul(A, X), B), Domain.equalsTo(0.0));
In this example the domain Domain.equalsTo
scales to apply to all the entries of the expression.
Another powerful case of vectorization and scaling domains is the ability to define a sequence of conic constraints in one go. Suppose we want to find an upper bound on the 2norm of a sequence of vectors, that is we want to express
Suppose that the vectors \(y_i\) are arranged in the rows of a matrix \(Y\). Then we can simply write:
t = M.variable();
M.constraint(Expr.hstack(Var.vrepeat(t, n), Y), Domain.inQCone());
Here, again, the conic domain Domain.inQCone
is by default applied to each row of the matrix separately, yielding the desired constraints in a loopfree way (the \(i\)th row is \((t,y_i)\)). The direction along which conic constraints are created within multidimensional expressions can be changed with Domain.axis
.
We recommend vectorizing the code whenever possible. It is not only more elegant and portable but also more efficient — loops are eliminated and the number of Fusion API calls is reduced.
6.8 Reoptimization¶
Between optimizations the user can modify the model in two ways:
Add new constraints with
Model.constraint
. This is useful for solving a sequence of optimization problems with more and more restrictions on the feasible set. See for example Section 10.9.Replace the objective with a new one. This is particularly useful when solving a sequence of problems with the same data but different objectives, for instance in multiobjective optimization. For simplicity, suppose we want to minimize \(f(x) = \gamma x + \beta y\), for varying choices of \(\gamma>0\). Then we could write:
gamma=[0., 0.5, 1.0]; % Choices for gamma beta=2.0; x = M.variable('x', 1, Domain.greaterThan(0.)); y = M.variable('y', 1, Domain.greaterThan(0.)); beta_y = Expr.mul(beta,y); for g = gamma M.objective( ObjectiveSense.Minimize, Expr.add(Expr.mul(g,x), beta_y) ); M.solve(); end
Add a new expression to an existing constraint (
Constraint.add
).
Otherwise all Fusion objects are immutable.