scipy.optimize.Bounds¶

class scipy.optimize.Bounds(lb, ub, keep_feasible=False)[source]¶

Bounds constraint on the variables.

The constraint has the general inequality form:

lb <= x <= ub

It is possible to use equal bounds to represent an equality constraint or infinite bounds to represent a one-sided constraint.

Parameters:

Parameters:	lb, ub : array_like, optional Lower and upper bounds on independent variables. Each array must have the same size as x or be a scalar, in which case a bound will be the same for all the variables. Set components of lb and ub equal to fix a variable. Use `np.inf` with an appropriate sign to disable bounds on all or some variables. 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.

lb, ub : array_like, optional: Lower and upper bounds on independent variables. Each array must have the same size as x or be a scalar, in which case a bound will be the same for all the variables. Set components of lb and ub equal to fix a variable. Use np.inf with an appropriate sign to disable bounds on all or some variables. 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.