Source code for quantecon.compute_fp

"""
Compute an approximate fixed point of a given operator T, starting from
specified initial condition v.

"""
import time
import warnings
import numpy as np
from numba import jit, generated_jit, types
from .game_theory.lemke_howson import _lemke_howson_tbl, _get_mixed_actions

def _print_after_skip(skip, it=None, dist=None, etime=None):
if it is None:
msg = "{i:<13}{d:<15}{t:<17}".format(i="Iteration",
d="Distance",
t="Elapsed (seconds)")
print(msg)
print("-" * len(msg))

return

if it % skip == 0:
if etime is None:
print("After {it} iterations dist is {d}".format(it=it, d=dist))

else:
# leave 4 spaces between columns if we have %3.3e in d, t
msg = "{i:<13}{d:<15.3e}{t:<18.3e}"
print(msg.format(i=it, d=dist, t=etime))

return

_convergence_msg = 'Converged in {iterate} steps'
_non_convergence_msg = \
'max_iter attained before convergence in compute_fixed_point'

def _is_approx_fp(T, v, error_tol, *args, **kwargs):
error = np.max(np.abs(T(v, *args, **kwargs) - v))
return error <= error_tol

[docs]def compute_fixed_point(T, v, error_tol=1e-3, max_iter=50, verbose=2, print_skip=5, method='iteration', *args, **kwargs): r""" Computes and returns an approximate fixed point of the function T. The default method 'iteration' simply iterates the function given an initial condition v and returns :math:T^k v when the condition :math:\lVert T^k v - T^{k-1} v\rVert \leq \mathrm{error\_tol} is satisfied or the number of iterations :math:k reaches max_iter. Provided that T is a contraction mapping or similar, :math:T^k v will be an approximation to the fixed point. The method 'imitation_game' uses the "imitation game algorithm" developed by McLennan and Tourky _, which internally constructs a sequence of two-player games called imitation games and utilizes their Nash equilibria, computed by the Lemke-Howson algorithm routine. It finds an approximate fixed point of T, a point :math:v^* such that :math:\lVert T(v) - v\rVert \leq \mathrm{error\_tol}, provided T is a function that satisfies the assumptions of Brouwer's fixed point theorm, i.e., a continuous function that maps a compact and convex set to itself. Parameters ---------- T : callable A callable object (e.g., function) that acts on v v : object An object such that T(v) is defined; modified in place if method='iteration' and v is an array error_tol : scalar(float), optional(default=1e-3) Error tolerance max_iter : scalar(int), optional(default=50) Maximum number of iterations verbose : scalar(int), optional(default=2) Level of feedback (0 for no output, 1 for warnings only, 2 for warning and residual error reports during iteration) print_skip : scalar(int), optional(default=5) How many iterations to apply between print messages (effective only when verbose=2) method : str, optional(default='iteration') str in {'iteration', 'imitation_game'}. Method of computing an approximate fixed point args, kwargs : Other arguments and keyword arguments that are passed directly to the function T each time it is called Returns ------- v : object The approximate fixed point References ---------- ..  A. McLennan and R. Tourky, "From Imitation Games to Kakutani," 2006. """ if max_iter < 1: raise ValueError('max_iter must be a positive integer') if verbose not in (0, 1, 2): raise ValueError('verbose should be 0, 1 or 2') if method not in ['iteration', 'imitation_game']: raise ValueError('invalid method') if method == 'imitation_game': is_approx_fp = \ lambda v: _is_approx_fp(T, v, error_tol, *args, **kwargs) v_star, converged, iterate = \ _compute_fixed_point_ig(T, v, max_iter, verbose, print_skip, is_approx_fp, *args, **kwargs) return v_star # method == 'iteration' iterate = 0 error = error_tol + 1 if verbose == 2: start_time = time.time() _print_after_skip(print_skip, it=None) while True: new_v = T(v, *args, **kwargs) iterate += 1 error = np.max(np.abs(new_v - v)) try: v[:] = new_v except TypeError: v = new_v if error <= error_tol or iterate >= max_iter: break if verbose == 2: etime = time.time() - start_time _print_after_skip(print_skip, iterate, error, etime) if verbose == 2: etime = time.time() - start_time print_skip = 1 _print_after_skip(print_skip, iterate, error, etime) if verbose >= 1: if error > error_tol: warnings.warn(_non_convergence_msg, RuntimeWarning) elif verbose == 2: print(_convergence_msg.format(iterate=iterate)) return v
def _compute_fixed_point_ig(T, v, max_iter, verbose, print_skip, is_approx_fp, *args, **kwargs): """ Implement the imitation game algorithm by McLennan and Tourky (2006) for computing an approximate fixed point of T. Parameters ---------- is_approx_fp : callable A callable with signature is_approx_fp(v) which determines whether v is an approximate fixed point with a bool return value (i.e., True or False) For the other parameters, see Parameters in compute_fixed_point. Returns ------- x_new : scalar(float) or ndarray(float) Approximate fixed point. converged : bool Whether the routine has converged. iterate : scalar(int) Number of iterations. """ if verbose == 2: start_time = time.time() _print_after_skip(print_skip, it=None) x_new = v y_new = T(x_new, *args, **kwargs) iterate = 1 converged = is_approx_fp(x_new) if converged or iterate >= max_iter: if verbose == 2: error = np.max(np.abs(y_new - x_new)) etime = time.time() - start_time print_skip = 1 _print_after_skip(print_skip, iterate, error, etime) if verbose >= 1: if not converged: warnings.warn(_non_convergence_msg, RuntimeWarning) elif verbose == 2: print(_convergence_msg.format(iterate=iterate)) return x_new, converged, iterate if verbose == 2: error = np.max(np.abs(y_new - x_new)) etime = time.time() - start_time _print_after_skip(print_skip, iterate, error, etime) # Length of the arrays to store the computed sequences of x and y. # If exceeded, reset to min(max_iter, buff_size*2). buff_size = 2**8 buff_size = min(max_iter, buff_size) shape = (buff_size,) + np.asarray(x_new).shape X, Y = np.empty(shape), np.empty(shape) X, Y = x_new, y_new x_new = Y tableaux = tuple(np.empty((buff_size, buff_size*2+1)) for i in range(2)) bases = tuple(np.empty(buff_size, dtype=int) for i in range(2)) max_piv = 10**6 # Max number of pivoting steps in _lemke_howson_tbl while True: y_new = T(x_new, *args, **kwargs) iterate += 1 converged = is_approx_fp(x_new) if converged or iterate >= max_iter: break if verbose == 2: error = np.max(np.abs(y_new - x_new)) etime = time.time() - start_time _print_after_skip(print_skip, iterate, error, etime) try: X[iterate-1] = x_new Y[iterate-1] = y_new except IndexError: buff_size = min(max_iter, buff_size*2) shape = (buff_size,) + X.shape[1:] X_tmp, Y_tmp = X, Y X, Y = np.empty(shape), np.empty(shape) X[:X_tmp.shape], Y[:Y_tmp.shape] = X_tmp, Y_tmp X[iterate-1], Y[iterate-1] = x_new, y_new tableaux = tuple(np.empty((buff_size, buff_size*2+1)) for i in range(2)) bases = tuple(np.empty(buff_size, dtype=int) for i in range(2)) m = iterate tableaux_curr = tuple(tableau[:m, :2*m+1] for tableau in tableaux) bases_curr = tuple(basis[:m] for basis in bases) _initialize_tableaux_ig(X[:m], Y[:m], tableaux_curr, bases_curr) converged, num_iter = _lemke_howson_tbl( tableaux_curr, bases_curr, init_pivot=m-1, max_iter=max_piv ) _, rho = _get_mixed_actions(tableaux_curr, bases_curr) if Y.ndim <= 2: x_new = rho.dot(Y[:m]) else: shape_Y = Y.shape Y_2d = Y.reshape(shape_Y, np.prod(shape_Y[1:])) x_new = rho.dot(Y_2d[:m]).reshape(shape_Y[1:]) if verbose == 2: error = np.max(np.abs(y_new - x_new)) etime = time.time() - start_time print_skip = 1 _print_after_skip(print_skip, iterate, error, etime) if verbose >= 1: if not converged: warnings.warn(_non_convergence_msg, RuntimeWarning) elif verbose == 2: print(_convergence_msg.format(iterate=iterate)) return x_new, converged, iterate @jit(nopython=True) def _initialize_tableaux_ig(X, Y, tableaux, bases): """ Given sequences X and Y of ndarrays, initialize the tableau and basis arrays in place for the "geometric" imitation game as defined in McLennan and Tourky (2006), to be passed to _lemke_howson_tbl. Parameters ---------- X, Y : ndarray(float) Arrays of the same shape (m, n). tableaux : tuple(ndarray(float, ndim=2)) Tuple of two arrays to be used to store the tableaux, of shape (2m, 2m). Modified in place. bases : tuple(ndarray(int, ndim=1)) Tuple of two arrays to be used to store the bases, of shape (m,). Modified in place. Returns ------- tableaux : tuple(ndarray(float, ndim=2)) View to tableaux. bases : tuple(ndarray(int, ndim=1)) View to bases`. """ m = X.shape min_ = np.zeros(m) # Mover for i in range(m): for j in range(2*m): if j == i or j == i + m: tableaux[i, j] = 1 else: tableaux[i, j] = 0 # Right hand side tableaux[i, 2*m] = 1 # Imitator for i in range(m): # Slack variables for j in range(m): if j == i: tableaux[i, j] = 1 else: tableaux[i, j] = 0 # Payoff variables for j in range(m): d = X[i] - Y[j] tableaux[i, m+j] = _square_sum(d) * (-1) if tableaux[i, m+j] < min_[j]: min_[j] = tableaux[i, m+j] # Right hand side tableaux[i, 2*m] = 1 # Shift the payoff values for i in range(m): for j in range(m): tableaux[i, m+j] -= min_[j] tableaux[i, m+j] += 1 for pl, start in enumerate([m, 0]): for i in range(m): bases[pl][i] = start + i return tableaux, bases @generated_jit(nopython=True, cache=True) def _square_sum(a): if isinstance(a, types.Number): return lambda a: a**2 elif isinstance(a, types.Array): return _square_sum_array def _square_sum_array(a): # pragma: no cover sum_ = 0 for x in a.flat: sum_ += x**2 return sum_