251 lines
7.5 KiB
Python
251 lines
7.5 KiB
Python
"""The adaptation of Trust Region Reflective algorithm for a linear
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least-squares problem."""
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from __future__ import division, print_function, absolute_import
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import numpy as np
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from numpy.linalg import norm
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from scipy.linalg import qr, solve_triangular
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from scipy.sparse.linalg import lsmr
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from scipy.optimize import OptimizeResult
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from .givens_elimination import givens_elimination
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from .common import (
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EPS, step_size_to_bound, find_active_constraints, in_bounds,
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make_strictly_feasible, build_quadratic_1d, evaluate_quadratic,
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minimize_quadratic_1d, CL_scaling_vector, reflective_transformation,
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print_header_linear, print_iteration_linear, compute_grad,
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regularized_lsq_operator, right_multiplied_operator)
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def regularized_lsq_with_qr(m, n, R, QTb, perm, diag, copy_R=True):
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"""Solve regularized least squares using information from QR-decomposition.
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The initial problem is to solve the following system in a least-squares
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sense:
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::
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A x = b
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D x = 0
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Where D is diagonal matrix. The method is based on QR decomposition
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of the form A P = Q R, where P is a column permutation matrix, Q is an
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orthogonal matrix and R is an upper triangular matrix.
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Parameters
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----------
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m, n : int
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Initial shape of A.
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R : ndarray, shape (n, n)
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Upper triangular matrix from QR decomposition of A.
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QTb : ndarray, shape (n,)
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First n components of Q^T b.
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perm : ndarray, shape (n,)
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Array defining column permutation of A, such that i-th column of
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P is perm[i]-th column of identity matrix.
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diag : ndarray, shape (n,)
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Array containing diagonal elements of D.
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Returns
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-------
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x : ndarray, shape (n,)
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Found least-squares solution.
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"""
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if copy_R:
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R = R.copy()
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v = QTb.copy()
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givens_elimination(R, v, diag[perm])
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abs_diag_R = np.abs(np.diag(R))
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threshold = EPS * max(m, n) * np.max(abs_diag_R)
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nns, = np.nonzero(abs_diag_R > threshold)
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R = R[np.ix_(nns, nns)]
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v = v[nns]
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x = np.zeros(n)
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x[perm[nns]] = solve_triangular(R, v)
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return x
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def backtracking(A, g, x, p, theta, p_dot_g, lb, ub):
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"""Find an appropriate step size using backtracking line search."""
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alpha = 1
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while True:
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x_new, _ = reflective_transformation(x + alpha * p, lb, ub)
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step = x_new - x
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cost_change = -evaluate_quadratic(A, g, step)
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if cost_change > -0.1 * alpha * p_dot_g:
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break
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alpha *= 0.5
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active = find_active_constraints(x_new, lb, ub)
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if np.any(active != 0):
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x_new, _ = reflective_transformation(x + theta * alpha * p, lb, ub)
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x_new = make_strictly_feasible(x_new, lb, ub, rstep=0)
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step = x_new - x
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cost_change = -evaluate_quadratic(A, g, step)
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return x, step, cost_change
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def select_step(x, A_h, g_h, c_h, p, p_h, d, lb, ub, theta):
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"""Select the best step according to Trust Region Reflective algorithm."""
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if in_bounds(x + p, lb, ub):
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return p
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p_stride, hits = step_size_to_bound(x, p, lb, ub)
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r_h = np.copy(p_h)
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r_h[hits.astype(bool)] *= -1
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r = d * r_h
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# Restrict step, such that it hits the bound.
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p *= p_stride
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p_h *= p_stride
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x_on_bound = x + p
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# Find the step size along reflected direction.
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r_stride_u, _ = step_size_to_bound(x_on_bound, r, lb, ub)
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# Stay interior.
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r_stride_l = (1 - theta) * r_stride_u
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r_stride_u *= theta
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if r_stride_u > 0:
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a, b, c = build_quadratic_1d(A_h, g_h, r_h, s0=p_h, diag=c_h)
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r_stride, r_value = minimize_quadratic_1d(
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a, b, r_stride_l, r_stride_u, c=c)
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r_h = p_h + r_h * r_stride
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r = d * r_h
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else:
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r_value = np.inf
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# Now correct p_h to make it strictly interior.
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p_h *= theta
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p *= theta
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p_value = evaluate_quadratic(A_h, g_h, p_h, diag=c_h)
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ag_h = -g_h
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ag = d * ag_h
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ag_stride_u, _ = step_size_to_bound(x, ag, lb, ub)
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ag_stride_u *= theta
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a, b = build_quadratic_1d(A_h, g_h, ag_h, diag=c_h)
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ag_stride, ag_value = minimize_quadratic_1d(a, b, 0, ag_stride_u)
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ag *= ag_stride
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if p_value < r_value and p_value < ag_value:
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return p
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elif r_value < p_value and r_value < ag_value:
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return r
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else:
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return ag
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def trf_linear(A, b, x_lsq, lb, ub, tol, lsq_solver, lsmr_tol, max_iter,
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verbose):
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m, n = A.shape
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x, _ = reflective_transformation(x_lsq, lb, ub)
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x = make_strictly_feasible(x, lb, ub, rstep=0.1)
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if lsq_solver == 'exact':
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QT, R, perm = qr(A, mode='economic', pivoting=True)
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QT = QT.T
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if m < n:
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R = np.vstack((R, np.zeros((n - m, n))))
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QTr = np.zeros(n)
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k = min(m, n)
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elif lsq_solver == 'lsmr':
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r_aug = np.zeros(m + n)
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auto_lsmr_tol = False
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if lsmr_tol is None:
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lsmr_tol = 1e-2 * tol
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elif lsmr_tol == 'auto':
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auto_lsmr_tol = True
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r = A.dot(x) - b
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g = compute_grad(A, r)
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cost = 0.5 * np.dot(r, r)
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initial_cost = cost
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termination_status = None
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step_norm = None
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cost_change = None
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if max_iter is None:
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max_iter = 100
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if verbose == 2:
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print_header_linear()
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for iteration in range(max_iter):
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v, dv = CL_scaling_vector(x, g, lb, ub)
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g_scaled = g * v
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g_norm = norm(g_scaled, ord=np.inf)
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if g_norm < tol:
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termination_status = 1
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if verbose == 2:
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print_iteration_linear(iteration, cost, cost_change,
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step_norm, g_norm)
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if termination_status is not None:
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break
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diag_h = g * dv
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diag_root_h = diag_h ** 0.5
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d = v ** 0.5
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g_h = d * g
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A_h = right_multiplied_operator(A, d)
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if lsq_solver == 'exact':
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QTr[:k] = QT.dot(r)
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p_h = -regularized_lsq_with_qr(m, n, R * d[perm], QTr, perm,
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diag_root_h, copy_R=False)
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elif lsq_solver == 'lsmr':
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lsmr_op = regularized_lsq_operator(A_h, diag_root_h)
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r_aug[:m] = r
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if auto_lsmr_tol:
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eta = 1e-2 * min(0.5, g_norm)
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lsmr_tol = max(EPS, min(0.1, eta * g_norm))
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p_h = -lsmr(lsmr_op, r_aug, atol=lsmr_tol, btol=lsmr_tol)[0]
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p = d * p_h
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p_dot_g = np.dot(p, g)
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if p_dot_g > 0:
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termination_status = -1
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theta = 1 - min(0.005, g_norm)
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step = select_step(x, A_h, g_h, diag_h, p, p_h, d, lb, ub, theta)
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cost_change = -evaluate_quadratic(A, g, step)
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# Perhaps almost never executed, the idea is that `p` is descent
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# direction thus we must find acceptable cost decrease using simple
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# "backtracking", otherwise algorithm's logic would break.
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if cost_change < 0:
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x, step, cost_change = backtracking(
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A, g, x, p, theta, p_dot_g, lb, ub)
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else:
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x = make_strictly_feasible(x + step, lb, ub, rstep=0)
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step_norm = norm(step)
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r = A.dot(x) - b
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g = compute_grad(A, r)
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if cost_change < tol * cost:
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termination_status = 2
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cost = 0.5 * np.dot(r, r)
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if termination_status is None:
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termination_status = 0
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active_mask = find_active_constraints(x, lb, ub, rtol=tol)
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return OptimizeResult(
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x=x, fun=r, cost=cost, optimality=g_norm, active_mask=active_mask,
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nit=iteration + 1, status=termination_status,
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initial_cost=initial_cost)
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