code: def bvls(A, b, x_lsq, lb, ub, tol, max_iter, verbose):
      m, n = A.shape
  
      x = x_lsq.copy()
      on_bound = np.zeros(n)
  
      mask = x < lb
      x[mask] = lb[mask]
      on_bound[mask] = -1
  
      mask = x > ub
      x[mask] = ub[mask]
      on_bound[mask] = 1
  
      free_set = on_bound == 0
      active_set = ~free_set
      (free_set,) = np.nonzero(free_set)
  
      r = A.dot(x) - b
      cost = 0.5 * np.dot(r, r)
      initial_cost = cost
      g = A.T.dot(r)
  
      cost_change = None
      step_norm = None
      iteration = 0
  
      if verbose == 2:
          print_header_linear()
  
      # This is the initialization loop. The requirement is that the
      # least-squares solution on free variables is feasible before BVLS starts.
      # One possible initialization is to set all variables to lower or upper
      # bounds, but many iterations may be required from this state later on.
      # The implemented ad-hoc procedure which intuitively should give a better
      # initial state: find the least-squares solution on current free variables,
      # if its feasible then stop, otherwise set violating variables to
      # corresponding bounds and continue on the reduced set of free variables.
  
      while free_set.size > 0:
          if verbose == 2:
              optimality = compute_kkt_optimality(g, on_bound)
              print_iteration_linear(
                  iteration, cost, cost_change, step_norm, optimality
              )
  
          iteration += 1
          x_free_old = x[free_set].copy()
  
          A_free = A[:, free_set]
          b_free = b - A.dot(x * active_set)
          z = lstsq(A_free, b_free, rcond=-1)[0]
  
          lbv = z < lb[free_set]
          ubv = z > ub[free_set]
          v = lbv | ubv
  
          if np.any(lbv):
              ind = free_set[lbv]
              x[ind] = lb[ind]
              active_set[ind] = True
              on_bound[ind] = -1
  
          if np.any(ubv):
              ind = free_set[ubv]
              x[ind] = ub[ind]
              active_set[ind] = True
              on_bound[ind] = 1
  
          ind = free_set[~v]
          x[ind] = z[~v]
  
          r = A.dot(x) - b
          cost_new = 0.5 * np.dot(r, r)
          cost_change = cost - cost_new
          cost = cost_new
          g = A.T.dot(r)
          step_norm = norm(x[free_set] - x_free_old)
  
          if np.any(v):
              free_set = free_set[~v]
          else:
              break
  
      if max_iter is None:
          max_iter = n
      max_iter += iteration
  
      termination_status = None
  
      # Main BVLS loop.
  
      optimality = compute_kkt_optimality(g, on_bound)
      for iteration in range(iteration, max_iter):
          if verbose == 2:
              print_iteration_linear(
                  iteration, cost, cost_change, step_norm, optimality
              )
  
          if optimality < tol:
              termination_status = 1
  
          if termination_status is not None:
              break
  
          move_to_free = np.argmax(g * on_bound)
          on_bound[move_to_free] = 0
          free_set = on_bound == 0
          active_set = ~free_set
          (free_set,) = np.nonzero(free_set)
  
          x_free = x[free_set]
          x_free_old = x_free.copy()
          lb_free = lb[free_set]
          ub_free = ub[free_set]
  
          A_free = A[:, free_set]
          b_free = b - A.dot(x * active_set)
          z = lstsq(A_free, b_free, rcond=-1)[0]
  
          (lbv,) = np.nonzero(z < lb_free)
          (ubv,) = np.nonzero(z > ub_free)
          v = np.hstack((lbv, ubv))
  
          if v.size > 0:
              alphas = np.hstack(
                  (
                      lb_free[lbv] - x_free[lbv],
                      ub_free[ubv] - x_free[ubv],
                  )
              ) / (z[v] - x_free[v])
  
              i = np.argmin(alphas)
              i_free = v[i]
              alpha = alphas[i]
  
              x_free *= 1 - alpha
              x_free += alpha * z
  
              if i < lbv.size:
                  on_bound[free_set[i_free]] = -1
              else:
                  on_bound[free_set[i_free]] = 1
          else:
              x_free = z
  
          x[free_set] = x_free
          step_norm = norm(x_free - x_free_old)
  
          r = A.dot(x) - b
          cost_new = 0.5 * np.dot(r, r)
          cost_change = cost - cost_new
  
          if cost_change < tol * cost:
              termination_status = 2
          cost = cost_new
  
          g = A.T.dot(r)
          optimality = compute_kkt_optimality(g, on_bound)
  
      if termination_status is None:
          termination_status = 0
  
      return OptimizeResult(
          x=x,
          fun=r,
          cost=cost,
          optimality=optimality,
          active_mask=on_bound,
          nit=iteration + 1,
          status=termination_status,
          initial_cost=initial_cost,
      )
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from numpy.linalg import norm, lstsq
  from scipy.optimize import OptimizeResult
  from .common import print_header_linear, print_iteration_linear
label_desc: Use image node_id 2 for calling a global function with example usage: bvls(A, b, x_lsq, lb, ub, tol, max_iter, verbose) and returns: OptimizeResult