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