def work(gi, p, s): G, C, M, k = common.get_stuff(gi) sample_g = np.linspace(1.45, 1.25, num=p + 2) sample_g = sample_g[1:p + 1] sample_b = np.linspace(0.14, 0, num=p + 2) sample_b = sample_b[1:p + 1] samples = np.append(sample_g, sample_b) bounds = [[0, pi / 2] if j < p else [0, pi / 2] for j in range(2 * p)] kwargs = {'method': 'L-BFGS-B', 'args': (G, C, M, k, p), 'bounds': bounds} optimal = basinhopping(qaoa, samples, minimizer_kwargs=kwargs, niter=s, disp=False) return -optimal.fun
def work(gi, p, s): G, C, M, k = common.get_stuff(gi) sample_g, sample_b = common.MLHS(p, s, 0, pi / 2, 0, pi) bounds = [[0, pi / 2] if j < p else [0, pi] for j in range(2 * p)] best = -1 for i in range(s): kwargs = { 'method': 'L-BFGS-B', 'args': (G, C, M, k, p), 'bounds': bounds } optimal = basinhopping(qaoa, [sample_g[i], sample_b[i]], minimizer_kwargs=kwargs, niter=2, disp=False) if -optimal.fun > best: best = -optimal.fun return best
def work(gi, p, s): random.seed(random.randint(1,10000) + rank) G, C, M, k = common.get_stuff(gi) sample_g, sample_b = common.MLHS(p, s, 0, 0.6, 2.9, pi) bounds = [[0,0.6] if j < p else [2.9,pi] for j in range(2*p)] eps = 0.001 best, angles = -1, [] for i in range(s): kwargs = {'method': 'L-BFGS-B', 'args': (G, C, M, k, p), 'bounds': bounds} optimal = basinhopping(qaoa, [sample_g[i], sample_b[i]], minimizer_kwargs=kwargs, niter=0, disp=False) if -optimal.fun > best + eps: best = -optimal.fun angles = [list(optimal.x)] elif -optimal.fun > best - eps: angles.append(list(optimal.x)) #print(str(rank) + ', ' + str(best) + ', ' + str(angles)) return best, angles
def work(gi, p, s): G, C, M, k = common.get_stuff(gi) sample_g, sample_b = common.MLHS(p, s, 0, pi / 2, 0, pi) bounds = [[0, pi / 2] if j < p else [0, pi] for j in range(2 * p)] eps = 0.001 best, angles = -1, [] for i in range(s): kwargs = { 'method': 'L-BFGS-B', 'args': (G, C, M, k, p), 'bounds': bounds } optimal = basinhopping(qaoa, [sample_g[i], sample_b[i]], minimizer_kwargs=kwargs, niter=2, disp=False) if -optimal.fun > best + eps: best = -optimal.fun angles = [list(optimal.x)] elif -optimal.fun > best - eps: angles.append(list(optimal.x)) return best, angles
def classical(gi, p, s): G, C, M, k = common.get_stuff(gi) data = [] num_trials = 20 n_iter = 2 for i in range(s): print('\ts = ' + str(i) + '\t' + str(datetime.datetime.now().time())) sample_g, sample_b = common.MLHS(p, num_trials, 0, pi / 2, 0, pi) init = [[0, pi / 2] if i < p else [0, pi] for i in range(2 * p)] exp = -1 for i in range(num_trials): kwargs = { 'method': 'L-BFGS-B', 'args': (G, C, M, k, p), 'bounds': init } optimal = basinhopping(qaoa, [sample_g[i], sample_b[i]], minimizer_kwargs=kwargs, niter=n_iter, disp=False) if -optimal.fun > exp: exp = -optimal.fun data.append(exp) return data