The constraint has the general inequality form:
lb <= A.dot(x) <= ub
Here the vector of independent variables x is passed as ndarray of shape (n,) and the matrix A has shape (m, n).
It is possible to use equal bounds to represent an equality constraint or infinite bounds to represent a one-sided constraint.
Matrix defining the constraint.
Lower and upper bounds on the constraint. Each array must have the shape (m,) or be a scalar, in the latter case a bound will be the same for all components of the constraint. Use np.inf
with an appropriate sign to specify a one-sided constraint. Set components of :None:None:`lb` and :None:None:`ub` equal to represent an equality constraint. Note that you can mix constraints of different types: interval, one-sided or equality, by setting different components of :None:None:`lb` and :None:None:`ub` as necessary.
Whether to keep the constraint components feasible throughout iterations. A single value set this property for all components. Default is False. Has no effect for equality constraints.
Linear constraint on the variables.
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.optimize._minimize.minimize
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