package scipy

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 matrix, shape (m, n) Matrix defining the constraint. lb, ub : array_like 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 `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. keep_feasible : 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.

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