LinearConstraint#
- class scipy.optimize.LinearConstraint(A, lb=-inf, ub=inf, keep_feasible=False)[source]#
- Linear constraint on the variables. - 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. - Parameters:
- A{array_like, sparse array}, shape (m, n)
- Matrix defining the constraint. 
- lb, ubdense array_like, optional
- Lower and upper limits 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.infwith an appropriate sign to specify a one-sided constraint. Set components of lb and 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 lb and ub as necessary. Defaults to- lb = -np.infand- ub = np.inf(no limits).
- keep_feasibledense array_like of bool, optional
- 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. 
 
 - Methods - residual(x)- Calculate the residual between the constraint function and the limits