def _dmp_simplify_gcd(f, g, u, K): """Try to eliminate `x_0` from GCD computation in `K[X]`. """ df = dmp_degree(f, u) dg = dmp_degree(g, u) if df > 0 and dg > 0: return if not (df or dg): F = dmp_LC(f, K) G = dmp_LC(g, K) else: if not df: F = dmp_LC(f, K) G = dmp_content(g, u, K) else: F = dmp_content(f, u, K) G = dmp_LC(g, K) v = u - 1 h = dmp_gcd(F, G, v, K) cff = [dmp_quo(cf, h, v, K) for cf in f] cfg = [dmp_quo(cg, h, v, K) for cg in g] return [h], cff, cfg
def dmp_pdiv(f, g, u, K): """ Polynomial pseudo-division in ``K[X]``. Examples ======== >>> from diofant.polys import ring, ZZ >>> R, x,y = ring("x,y", ZZ) >>> R.dmp_pdiv(x**2 + x*y, 2*x + 2) (2*x + 2*y - 2, -4*y + 4) """ if not u: return dup_pdiv(f, g, K) df = dmp_degree(f, u) dg = dmp_degree(g, u) if dg < 0: raise ZeroDivisionError("polynomial division") q, r, dr = dmp_zero(u), f, df if df < dg: return q, r N = df - dg + 1 lc_g = dmp_LC(g, K) while True: lc_r = dmp_LC(r, K) j, N = dr - dg, N - 1 Q = dmp_mul_term(q, lc_g, 0, u, K) q = dmp_add_term(Q, lc_r, j, u, K) R = dmp_mul_term(r, lc_g, 0, u, K) G = dmp_mul_term(g, lc_r, j, u, K) r = dmp_sub(R, G, u, K) _dr, dr = dr, dmp_degree(r, u) if dr < dg: break elif not (dr < _dr): raise PolynomialDivisionFailed(f, g, K) c = dmp_pow(lc_g, N, u - 1, K) q = dmp_mul_term(q, c, 0, u, K) r = dmp_mul_term(r, c, 0, u, K) return q, r
def test_dmp_LC(): assert dmp_LC([[]], ZZ) == [] assert dmp_LC([], ZZ) == 0 assert dmp_LC([2, 3, 4, 5], ZZ) == 2 assert dmp_LC([[2, 3, 4], [5]], ZZ) == [2, 3, 4] assert dmp_LC([[[]]], ZZ) == [[]] assert dmp_LC([[[2], [3, 4]], [[5]]], ZZ) == [[2], [3, 4]] assert dmp_LC([3, 0, 1], ZZ) == 3 assert dmp_LC([1], ZZ) == 1 assert dmp_LC([1, 2], ZZ) == 1
def dmp_ff_div(f, g, u, K): """ Polynomial division with remainder over a field. Examples ======== >>> from diofant.polys import ring, QQ >>> R, x,y = ring("x,y", QQ) >>> R.dmp_ff_div(x**2 + x*y, 2*x + 2) (1/2*x + 1/2*y - 1/2, -y + 1) """ if not u: return dup_ff_div(f, g, K) df = dmp_degree(f, u) dg = dmp_degree(g, u) if dg < 0: raise ZeroDivisionError("polynomial division") q, r, dr = dmp_zero(u), f, df if df < dg: return q, r lc_g, v = dmp_LC(g, K), u - 1 while True: lc_r = dmp_LC(r, K) c, R = dmp_ff_div(lc_r, lc_g, v, K) if not dmp_zero_p(R, v): break j = dr - dg q = dmp_add_term(q, c, j, u, K) h = dmp_mul_term(g, c, j, u, K) r = dmp_sub(r, h, u, K) _dr, dr = dr, dmp_degree(r, u) if dr < dg: break elif not (dr < _dr): raise PolynomialDivisionFailed(f, g, K) return q, r
def dmp_eval(f, a, u, K): """ Evaluate a polynomial at ``x_0 = a`` in ``K[X]`` using the Horner scheme. Examples ======== >>> from diofant.polys import ring, ZZ >>> R, x,y = ring("x,y", ZZ) >>> R.dmp_eval(2*x*y + 3*x + y + 2, 2) 5*y + 8 """ if not u: return dup_eval(f, a, K) if not a: return dmp_TC(f, K) result, v = dmp_LC(f, K), u - 1 for coeff in f[1:]: result = dmp_mul_ground(result, a, v, K) result = dmp_add(result, coeff, v, K) return result
def dmp_discriminant(f, u, K): """ Computes discriminant of a polynomial in `K[X]`. Examples ======== >>> from diofant.polys import ring, ZZ >>> R, x,y,z,t = ring("x,y,z,t", ZZ) >>> R.dmp_discriminant(x**2*y + x*z + t) -4*y*t + z**2 """ if not u: return dup_discriminant(f, K) d, v = dmp_degree(f, u), u - 1 if d <= 0: return dmp_zero(v) else: s = (-1)**((d * (d - 1)) // 2) c = dmp_LC(f, K) r = dmp_resultant(f, dmp_diff(f, 1, u, K), u, K) c = dmp_mul_ground(c, K(s), v, K) return dmp_quo(r, c, v, K)
def dmp_content(f, u, K): """ Returns GCD of multivariate coefficients. Examples ======== >>> from diofant.polys import ring, ZZ >>> R, x,y, = ring("x,y", ZZ) >>> R.dmp_content(2*x*y + 6*x + 4*y + 12) 2*y + 6 """ cont, v = dmp_LC(f, K), u - 1 if dmp_zero_p(f, u): return cont for c in f[1:]: cont = dmp_gcd(cont, c, v, K) if dmp_one_p(cont, v, K): break if K.is_negative(dmp_ground_LC(cont, v, K)): return dmp_neg(cont, v, K) else: return cont
def dmp_zz_wang_test_points(f, T, ct, A, u, K): """Wang/EEZ: Test evaluation points for suitability. """ if not dmp_eval_tail(dmp_LC(f, K), A, u - 1, K): raise EvaluationFailed('no luck') g = dmp_eval_tail(f, A, u, K) if not dup_sqf_p(g, K): raise EvaluationFailed('no luck') c, h = dup_primitive(g, K) if K.is_negative(dup_LC(h, K)): c, h = -c, dup_neg(h, K) v = u - 1 E = [dmp_eval_tail(t, A, v, K) for t, _ in T] D = dmp_zz_wang_non_divisors(E, c, ct, K) if D is not None: return c, h, E else: raise EvaluationFailed('no luck')
def test_dmp_LC(): assert dmp_LC([[]], ZZ) == [] assert dmp_LC([[2, 3, 4], [5]], ZZ) == [2, 3, 4] assert dmp_LC([[[]]], ZZ) == [[]] assert dmp_LC([[[2], [3, 4]], [[5]]], ZZ) == [[2], [3, 4]]
def dmp_inner_subresultants(f, g, u, K): """ Subresultant PRS algorithm in `K[X]`. Examples ======== >>> from diofant.polys import ring, ZZ >>> R, x,y = ring("x,y", ZZ) >>> f = 3*x**2*y - y**3 - 4 >>> g = x**2 + x*y**3 - 9 >>> a = 3*x*y**4 + y**3 - 27*y + 4 >>> b = -3*y**10 - 12*y**7 + y**6 - 54*y**4 + 8*y**3 + 729*y**2 - 216*y + 16 >>> prs = [f, g, a, b] >>> sres = [[1], [1], [3, 0, 0, 0, 0], [-3, 0, 0, -12, 1, 0, -54, 8, 729, -216, 16]] >>> R.dmp_inner_subresultants(f, g) == (prs, sres) True """ if not u: return dup_inner_subresultants(f, g, K) n = dmp_degree(f, u) m = dmp_degree(g, u) if n < m: f, g = g, f n, m = m, n if dmp_zero_p(f, u): return [], [] v = u - 1 if dmp_zero_p(g, u): return [f], [dmp_ground(K.one, v)] R = [f, g] d = n - m b = dmp_pow(dmp_ground(-K.one, v), d + 1, v, K) h = dmp_prem(f, g, u, K) h = dmp_mul_term(h, b, 0, u, K) lc = dmp_LC(g, K) c = dmp_pow(lc, d, v, K) S = [dmp_ground(K.one, v), c] c = dmp_neg(c, v, K) while not dmp_zero_p(h, u): k = dmp_degree(h, u) R.append(h) f, g, m, d = g, h, k, m - k b = dmp_mul(dmp_neg(lc, v, K), dmp_pow(c, d, v, K), v, K) h = dmp_prem(f, g, u, K) h = [dmp_quo(ch, b, v, K) for ch in h] lc = dmp_LC(g, K) if d > 1: p = dmp_pow(dmp_neg(lc, v, K), d, v, K) q = dmp_pow(c, d - 1, v, K) c = dmp_quo(p, q, v, K) else: c = dmp_neg(lc, v, K) S.append(dmp_neg(c, v, K)) return R, S
def dmp_zz_wang(f, u, K, mod=None, seed=None): """ Factor primitive square-free polynomials in `Z[X]`. Given a multivariate polynomial `f` in `Z[x_1,...,x_n]`, which is primitive and square-free in `x_1`, computes factorization of `f` into irreducibles over integers. The procedure is based on Wang's Enhanced Extended Zassenhaus algorithm. The algorithm works by viewing `f` as a univariate polynomial in `Z[x_2,...,x_n][x_1]`, for which an evaluation mapping is computed:: x_2 -> a_2, ..., x_n -> a_n where `a_i`, for `i = 2, ..., n`, are carefully chosen integers. The mapping is used to transform `f` into a univariate polynomial in `Z[x_1]`, which can be factored efficiently using Zassenhaus algorithm. The last step is to lift univariate factors to obtain true multivariate factors. For this purpose a parallel Hensel lifting procedure is used. The parameter ``seed`` is passed to _randint and can be used to seed randint (when an integer) or (for testing purposes) can be a sequence of numbers. References ========== .. [1] [Wang78]_ .. [2] [Geddes92]_ """ from diofant.utilities.randtest import _randint randint = _randint(seed) ct, T = dmp_zz_factor(dmp_LC(f, K), u - 1, K) b = dmp_zz_mignotte_bound(f, u, K) p = K(nextprime(b)) if mod is None: if u == 1: mod = 2 else: mod = 1 history, configs, A, r = set(), [], [K.zero] * u, None try: cs, s, E = dmp_zz_wang_test_points(f, T, ct, A, u, K) _, H = dup_zz_factor_sqf(s, K) r = len(H) if r == 1: return [f] configs = [(s, cs, E, H, A)] except EvaluationFailed: pass eez_num_configs = query('EEZ_NUMBER_OF_CONFIGS') eez_num_tries = query('EEZ_NUMBER_OF_TRIES') eez_mod_step = query('EEZ_MODULUS_STEP') while len(configs) < eez_num_configs: for _ in range(eez_num_tries): A = [K(randint(-mod, mod)) for _ in range(u)] if tuple(A) not in history: history.add(tuple(A)) else: continue try: cs, s, E = dmp_zz_wang_test_points(f, T, ct, A, u, K) except EvaluationFailed: continue _, H = dup_zz_factor_sqf(s, K) rr = len(H) if r is not None: if rr != r: # pragma: no cover if rr < r: configs, r = [], rr else: continue else: r = rr if r == 1: return [f] configs.append((s, cs, E, H, A)) if len(configs) == eez_num_configs: break else: mod += eez_mod_step s_norm, s_arg, i = None, 0, 0 for s, _, _, _, _ in configs: _s_norm = dup_max_norm(s, K) if s_norm is not None: if _s_norm < s_norm: s_norm = _s_norm s_arg = i else: s_norm = _s_norm i += 1 _, cs, E, H, A = configs[s_arg] orig_f = f try: f, H, LC = dmp_zz_wang_lead_coeffs(f, T, cs, E, H, A, u, K) factors = dmp_zz_wang_hensel_lifting(f, H, LC, A, p, u, K) except ExtraneousFactors: # pragma: no cover if query('EEZ_RESTART_IF_NEEDED'): return dmp_zz_wang(orig_f, u, K, mod + 1) else: raise ExtraneousFactors( "we need to restart algorithm with better parameters") negative, result = 0, [] for f in factors: _, f = dmp_ground_primitive(f, u, K) if K.is_negative(dmp_ground_LC(f, u, K)): f = dmp_neg(f, u, K) result.append(f) return result