class_docstr: 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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code: def __init__(self, A, lb, ub, keep_feasible=False):
      self.A = A
      self.lb = lb
      self.ub = ub
      self.keep_feasible = keep_feasible
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from ._hessian_update_strategy import BFGS
  from ._differentiable_functions import VectorFunction, LinearVectorFunction, IdentityVectorFunction
  from .optimize import OptimizeWarning
  from warnings import warn
  from scipy.sparse import issparse
label_desc: Use image node_id 1 to create a new LinearConstraint object from inherited base classes: object with example: obj = LinearConstraint(A, lb, ub, keep_feasible)