X-Git-Url: http://review.tizen.org/git/?a=blobdiff_plain;f=isl_equalities.c;h=0632ddd8a9ef41126522dc52435b8c5758962b57;hb=b3acbdec6ee87a2c00278f47556b7de0653a7687;hp=c8489a0c3259e490e3158b1ab8270e9c19bc8ebb;hpb=81c62e82fb9761b324708ef668ef44d0e65893b8;p=platform%2Fupstream%2Fisl.git diff --git a/isl_equalities.c b/isl_equalities.c index c8489a0..0632ddd 100644 --- a/isl_equalities.c +++ b/isl_equalities.c @@ -3,6 +3,367 @@ #include "isl_map_private.h" #include "isl_equalities.h" +/* Given a set of modulo constraints + * + * c + A y = 0 mod d + * + * this function computes a particular solution y_0 + * + * The input is given as a matrix B = [ c A ] and a vector d. + * + * The output is matrix containing the solution y_0 or + * a zero-column matrix if the constraints admit no integer solution. + * + * The given set of constrains is equivalent to + * + * c + A y = -D x + * + * with D = diag d and x a fresh set of variables. + * Reducing both c and A modulo d does not change the + * value of y in the solution and may lead to smaller coefficients. + * Let M = [ D A ] and [ H 0 ] = M U, the Hermite normal form of M. + * Then + * [ x ] + * M [ y ] = - c + * and so + * [ x ] + * [ H 0 ] U^{-1} [ y ] = - c + * Let + * [ A ] [ x ] + * [ B ] = U^{-1} [ y ] + * then + * H A + 0 B = -c + * + * so B may be chosen arbitrarily, e.g., B = 0, and then + * + * [ x ] = [ -c ] + * U^{-1} [ y ] = [ 0 ] + * or + * [ x ] [ -c ] + * [ y ] = U [ 0 ] + * specifically, + * + * y = U_{2,1} (-c) + * + * If any of the coordinates of this y are non-integer + * then the constraints admit no integer solution and + * a zero-column matrix is returned. + */ +static struct isl_mat *particular_solution(struct isl_ctx *ctx, + struct isl_mat *B, struct isl_vec *d) +{ + int i, j; + struct isl_mat *M = NULL; + struct isl_mat *C = NULL; + struct isl_mat *U = NULL; + struct isl_mat *H = NULL; + struct isl_mat *cst = NULL; + struct isl_mat *T = NULL; + + M = isl_mat_alloc(ctx, B->n_row, B->n_row + B->n_col - 1); + C = isl_mat_alloc(ctx, 1 + B->n_row, 1); + if (!M || !C) + goto error; + isl_int_set_si(C->row[0][0], 1); + for (i = 0; i < B->n_row; ++i) { + isl_seq_clr(M->row[i], B->n_row); + isl_int_set(M->row[i][i], d->block.data[i]); + isl_int_neg(C->row[1 + i][0], B->row[i][0]); + isl_int_fdiv_r(C->row[1+i][0], C->row[1+i][0], M->row[i][i]); + for (j = 0; j < B->n_col - 1; ++j) + isl_int_fdiv_r(M->row[i][B->n_row + j], + B->row[i][1 + j], M->row[i][i]); + } + M = isl_mat_left_hermite(ctx, M, 0, &U, NULL); + if (!M || !U) + goto error; + H = isl_mat_sub_alloc(ctx, M->row, 0, B->n_row, 0, B->n_row); + H = isl_mat_lin_to_aff(ctx, H); + C = isl_mat_inverse_product(ctx, H, C); + if (!C) + goto error; + for (i = 0; i < B->n_row; ++i) { + if (!isl_int_is_divisible_by(C->row[1+i][0], C->row[0][0])) + break; + isl_int_divexact(C->row[1+i][0], C->row[1+i][0], C->row[0][0]); + } + if (i < B->n_row) + cst = isl_mat_alloc(ctx, B->n_row, 0); + else + cst = isl_mat_sub_alloc(ctx, C->row, 1, B->n_row, 0, 1); + T = isl_mat_sub_alloc(ctx, U->row, B->n_row, B->n_col - 1, 0, B->n_row); + cst = isl_mat_product(ctx, T, cst); + isl_mat_free(ctx, M); + isl_mat_free(ctx, C); + isl_mat_free(ctx, U); + return cst; +error: + isl_mat_free(ctx, M); + isl_mat_free(ctx, C); + isl_mat_free(ctx, U); + return NULL; +} + +/* Compute and return the matrix + * + * U_1^{-1} diag(d_1, 1, ..., 1) + * + * with U_1 the unimodular completion of the first (and only) row of B. + * The columns of this matrix generate the lattice that satisfies + * the single (linear) modulo constraint. + */ +static struct isl_mat *parameter_compression_1(struct isl_ctx *ctx, + struct isl_mat *B, struct isl_vec *d) +{ + struct isl_mat *U; + + U = isl_mat_alloc(ctx, B->n_col - 1, B->n_col - 1); + if (!U) + return NULL; + isl_seq_cpy(U->row[0], B->row[0] + 1, B->n_col - 1); + U = isl_mat_unimodular_complete(ctx, U, 1); + U = isl_mat_right_inverse(ctx, U); + if (!U) + return NULL; + isl_mat_col_mul(U, 0, d->block.data[0], 0); + U = isl_mat_lin_to_aff(ctx, U); + return U; +error: + isl_mat_free(ctx, U); + return NULL; +} + +/* Compute a common lattice of solutions to the linear modulo + * constraints specified by B and d. + * See also the documentation of isl_mat_parameter_compression. + * We put the matrix + * + * A = [ L_1^{-T} L_2^{-T} ... L_k^{-T} ] + * + * on a common denominator. This denominator D is the lcm of modulos d. + * Since L_i = U_i^{-1} diag(d_i, 1, ... 1), we have + * L_i^{-T} = U_i^T diag(d_i, 1, ... 1)^{-T} = U_i^T diag(1/d_i, 1, ..., 1). + * Putting this on the common denominator, we have + * D * L_i^{-T} = U_i^T diag(D/d_i, D, ..., D). + */ +static struct isl_mat *parameter_compression_multi(struct isl_ctx *ctx, + struct isl_mat *B, struct isl_vec *d) +{ + int i, j, k; + int ok; + isl_int D; + struct isl_mat *A = NULL, *U = NULL; + struct isl_mat *T; + unsigned size; + + isl_int_init(D); + + isl_vec_lcm(ctx, d, &D); + + size = B->n_col - 1; + A = isl_mat_alloc(ctx, size, B->n_row * size); + U = isl_mat_alloc(ctx, size, size); + if (!U || !A) + goto error; + for (i = 0; i < B->n_row; ++i) { + isl_seq_cpy(U->row[0], B->row[i] + 1, size); + U = isl_mat_unimodular_complete(ctx, U, 1); + if (!U) + goto error; + isl_int_divexact(D, D, d->block.data[i]); + for (k = 0; k < U->n_col; ++k) + isl_int_mul(A->row[k][i*size+0], D, U->row[0][k]); + isl_int_mul(D, D, d->block.data[i]); + for (j = 1; j < U->n_row; ++j) + for (k = 0; k < U->n_col; ++k) + isl_int_mul(A->row[k][i*size+j], + D, U->row[j][k]); + } + A = isl_mat_left_hermite(ctx, A, 0, NULL, NULL); + T = isl_mat_sub_alloc(ctx, A->row, 0, A->n_row, 0, A->n_row); + T = isl_mat_lin_to_aff(ctx, T); + isl_int_set(T->row[0][0], D); + T = isl_mat_right_inverse(ctx, T); + isl_assert(ctx, isl_int_is_one(T->row[0][0]), goto error); + T = isl_mat_transpose(ctx, T); + isl_mat_free(ctx, A); + isl_mat_free(ctx, U); + + isl_int_clear(D); + return T; +error: + isl_mat_free(ctx, A); + isl_mat_free(ctx, U); + isl_int_clear(D); + return NULL; +} + +/* Given a set of modulo constraints + * + * c + A y = 0 mod d + * + * this function returns an affine transformation T, + * + * y = T y' + * + * that bijectively maps the integer vectors y' to integer + * vectors y that satisfy the modulo constraints. + * + * This function is inspired by Section 2.5.3 + * of B. Meister, "Stating and Manipulating Periodicity in the Polytope + * Model. Applications to Program Analysis and Optimization". + * However, the implementation only follows the algorithm of that + * section for computing a particular solution and not for computing + * a general homogeneous solution. The latter is incomplete and + * may remove some valid solutions. + * Instead, we use an adaptation of the algorithm in Section 7 of + * B. Meister, S. Verdoolaege, "Polynomial Approximations in the Polytope + * Model: Bringing the Power of Quasi-Polynomials to the Masses". + * + * The input is given as a matrix B = [ c A ] and a vector d. + * Each element of the vector d corresponds to a row in B. + * The output is a lower triangular matrix. + * If no integer vector y satisfies the given constraints then + * a matrix with zero columns is returned. + * + * We first compute a particular solution y_0 to the given set of + * modulo constraints in particular_solution. If no such solution + * exists, then we return a zero-columned transformation matrix. + * Otherwise, we compute the generic solution to + * + * A y = 0 mod d + * + * That is we want to compute G such that + * + * y = G y'' + * + * with y'' integer, describes the set of solutions. + * + * We first remove the common factors of each row. + * In particular if gcd(A_i,d_i) != 1, then we divide the whole + * row i (including d_i) by this common factor. If afterwards gcd(A_i) != 1, + * then we divide this row of A by the common factor, unless gcd(A_i) = 0. + * In the later case, we simply drop the row (in both A and d). + * + * If there are no rows left in A, the G is the identity matrix. Otherwise, + * for each row i, we now determine the lattice of integer vectors + * that satisfies this row. Let U_i be the unimodular extension of the + * row A_i. This unimodular extension exists because gcd(A_i) = 1. + * The first component of + * + * y' = U_i y + * + * needs to be a multiple of d_i. Let y' = diag(d_i, 1, ..., 1) y''. + * Then, + * + * y = U_i^{-1} diag(d_i, 1, ..., 1) y'' + * + * for arbitrary integer vectors y''. That is, y belongs to the lattice + * generated by the columns of L_i = U_i^{-1} diag(d_i, 1, ..., 1). + * If there is only one row, then G = L_1. + * + * If there is more than one row left, we need to compute the intersection + * of the lattices. That is, we need to compute an L such that + * + * L = L_i L_i' for all i + * + * with L_i' some integer matrices. Let A be constructed as follows + * + * A = [ L_1^{-T} L_2^{-T} ... L_k^{-T} ] + * + * and computed the Hermite Normal Form of A = [ H 0 ] U + * Then, + * + * L_i^{-T} = H U_{1,i} + * + * or + * + * H^{-T} = L_i U_{1,i}^T + * + * In other words G = L = H^{-T}. + * To ensure that G is lower triangular, we compute and use its Hermite + * normal form. + * + * The affine transformation matrix returned is then + * + * [ 1 0 ] + * [ y_0 G ] + * + * as any y = y_0 + G y' with y' integer is a solution to the original + * modulo constraints. + */ +struct isl_mat *isl_mat_parameter_compression(struct isl_ctx *ctx, + struct isl_mat *B, struct isl_vec *d) +{ + int i; + struct isl_mat *cst = NULL; + struct isl_mat *T = NULL; + isl_int D; + + if (!B || !d) + goto error; + isl_assert(ctx, B->n_row == d->size, goto error); + cst = particular_solution(ctx, B, d); + if (!cst) + goto error; + if (cst->n_col == 0) { + T = isl_mat_alloc(ctx, B->n_col, 0); + isl_mat_free(ctx, cst); + isl_mat_free(ctx, B); + isl_vec_free(ctx, d); + return T; + } + isl_int_init(D); + /* Replace a*g*row = 0 mod g*m by row = 0 mod m */ + for (i = 0; i < B->n_row; ++i) { + isl_seq_gcd(B->row[i] + 1, B->n_col - 1, &D); + if (isl_int_is_one(D)) + continue; + if (isl_int_is_zero(D)) { + B = isl_mat_drop_rows(ctx, B, i, 1); + d = isl_vec_cow(ctx, d); + if (!B || !d) + goto error2; + isl_seq_cpy(d->block.data+i, d->block.data+i+1, + d->size - (i+1)); + d->size--; + i--; + continue; + } + B = isl_mat_cow(ctx, B); + if (!B) + goto error2; + isl_seq_scale_down(B->row[i] + 1, B->row[i] + 1, D, B->n_col-1); + isl_int_gcd(D, D, d->block.data[i]); + d = isl_vec_cow(ctx, d); + if (!d) + goto error2; + isl_int_divexact(d->block.data[i], d->block.data[i], D); + } + isl_int_clear(D); + if (B->n_row == 0) + T = isl_mat_identity(ctx, B->n_col); + else if (B->n_row == 1) + T = parameter_compression_1(ctx, B, d); + else + T = parameter_compression_multi(ctx, B, d); + T = isl_mat_left_hermite(ctx, T, 0, NULL, NULL); + if (!T) + goto error; + isl_mat_sub_copy(ctx, T->row + 1, cst->row, cst->n_row, 0, 0, 1); + isl_mat_free(ctx, cst); + isl_mat_free(ctx, B); + isl_vec_free(ctx, d); + return T; +error2: + isl_int_clear(D); +error: + isl_mat_free(ctx, cst); + isl_mat_free(ctx, B); + isl_vec_free(ctx, d); + return NULL; +} + /* Given a set of equalities * * M x - c = 0 @@ -88,7 +449,7 @@ struct isl_mat *isl_mat_variable_compression(struct isl_ctx *ctx, isl_mat_free(ctx, *T2); *T2 = NULL; } - return isl_mat_alloc(ctx, 1 + B->n_col, 0); + return isl_mat_alloc(ctx, 1 + dim, 0); } isl_seq_scale_down(TC->row[1+i], TC->row[1+i], TC->row[0][0], 1); } @@ -156,7 +517,7 @@ static struct isl_basic_set *compress_variables(struct isl_ctx *ctx, return isl_basic_set_set_to_empty(bset); } - bset = isl_basic_set_preimage(ctx, bset, T ? isl_mat_copy(ctx, TC) : TC); + bset = isl_basic_set_preimage(bset, T ? isl_mat_copy(ctx, TC) : TC); if (T) *T = TC; return bset; @@ -176,7 +537,7 @@ struct isl_basic_set *isl_basic_set_remove_equalities( return NULL; isl_assert(bset->ctx, isl_basic_set_n_param(bset) == 0, goto error); bset = isl_basic_set_gauss(bset, NULL); - if (F_ISSET(bset, ISL_BASIC_SET_EMPTY)) + if (ISL_F_ISSET(bset, ISL_BASIC_SET_EMPTY)) return bset; bset = compress_variables(bset->ctx, bset, T, T2); return bset;