Can a quadratic constraint be used in MATLAB?
Note that the matrix-oriented Gurobi APIs (C, MATLAB, and R) require the right-hand side of a quadratic constraint to be a constant, while the object-oriented APIs (C++, Java, .NET, and Python) allow arbitrary quadratic expressions on both sides of the comparator.
Which is the best constraint type to use?
The available constraint types are linear , SOS , quadratic (both convex and non-convex), and general . A linear constraint allows you to restrict the value of a linear expression. For example, you may require that any feasible solution satisfies the constraint .
Can a constraint take a non-zero value?
In an SOS constraint of type 1 (an SOS1 constraint), at most one variable in the specified list is allowed to take a non-zero value. In an SOS constraint of type 2 (an SOS2 constraint), at most two variables in the specified, ordered list are allowed to take a non-zero value, and those non-zero variables must be contiguous in the list.
What happens when you add a constraint to a model?
If you add a constraint that can’t be transformed into one of these forms, then with default settings you will get an error ( GRB_ERROR_Q_NOT_PSD) when you try to solve the model. Quadratic equality constraints are always non-convex; they will give a GRB_ERROR_QCP_EQUALITY_CONSTRAINT error with default settings.
Why are quadratic equality constraints always non convex?
Quadratic equality constraints are always non-convex; they will give a GRB_ERROR_QCP_EQUALITY_CONSTRAINT error with default settings. Why distinguish between quadratic constraints in this form and other types of quadratic constraints?
Are there any constraints on floating point arithmetic?
Tolerances can be tightened to reduce such violations, but there are limits to how small the violations can be — errors are inherent in floating-point arithmetic. The available constraint types are linear , SOS , quadratic (both convex and non-convex), and general .
When to put a quadratic constraint in a cell array?
Assume that x and f are column vectors. ( x is a column vector when the initial vector x0 is a column vector.) For consistency and easy indexing, place every quadratic constraint matrix in one cell array.
Can a quadratic constraint be reformulated for equality?
If you also have quadratic equality constraints, you can use essentially the same technique. The problem is the same, with the additional constraints Reformulate your constraints to use the Ji, pi , and qi variables. The lambda.eqnonlin (i) structure has the Lagrange multipliers for equality constraints.