2 * Copyright 2008-2009 Katholieke Universiteit Leuven
4 * Use of this software is governed by the MIT license
6 * Written by Sven Verdoolaege, K.U.Leuven, Departement
7 * Computerwetenschappen, Celestijnenlaan 200A, B-3001 Leuven, Belgium
10 #include <isl_mat_private.h>
12 #include "isl_map_private.h"
13 #include "isl_equalities.h"
15 /* Given a set of modulo constraints
19 * this function computes a particular solution y_0
21 * The input is given as a matrix B = [ c A ] and a vector d.
23 * The output is matrix containing the solution y_0 or
24 * a zero-column matrix if the constraints admit no integer solution.
26 * The given set of constrains is equivalent to
30 * with D = diag d and x a fresh set of variables.
31 * Reducing both c and A modulo d does not change the
32 * value of y in the solution and may lead to smaller coefficients.
33 * Let M = [ D A ] and [ H 0 ] = M U, the Hermite normal form of M.
39 * [ H 0 ] U^{-1} [ y ] = - c
42 * [ B ] = U^{-1} [ y ]
46 * so B may be chosen arbitrarily, e.g., B = 0, and then
49 * U^{-1} [ y ] = [ 0 ]
57 * If any of the coordinates of this y are non-integer
58 * then the constraints admit no integer solution and
59 * a zero-column matrix is returned.
61 static struct isl_mat
*particular_solution(struct isl_mat
*B
, struct isl_vec
*d
)
64 struct isl_mat
*M
= NULL
;
65 struct isl_mat
*C
= NULL
;
66 struct isl_mat
*U
= NULL
;
67 struct isl_mat
*H
= NULL
;
68 struct isl_mat
*cst
= NULL
;
69 struct isl_mat
*T
= NULL
;
71 M
= isl_mat_alloc(B
->ctx
, B
->n_row
, B
->n_row
+ B
->n_col
- 1);
72 C
= isl_mat_alloc(B
->ctx
, 1 + B
->n_row
, 1);
75 isl_int_set_si(C
->row
[0][0], 1);
76 for (i
= 0; i
< B
->n_row
; ++i
) {
77 isl_seq_clr(M
->row
[i
], B
->n_row
);
78 isl_int_set(M
->row
[i
][i
], d
->block
.data
[i
]);
79 isl_int_neg(C
->row
[1 + i
][0], B
->row
[i
][0]);
80 isl_int_fdiv_r(C
->row
[1+i
][0], C
->row
[1+i
][0], M
->row
[i
][i
]);
81 for (j
= 0; j
< B
->n_col
- 1; ++j
)
82 isl_int_fdiv_r(M
->row
[i
][B
->n_row
+ j
],
83 B
->row
[i
][1 + j
], M
->row
[i
][i
]);
85 M
= isl_mat_left_hermite(M
, 0, &U
, NULL
);
88 H
= isl_mat_sub_alloc(M
, 0, B
->n_row
, 0, B
->n_row
);
89 H
= isl_mat_lin_to_aff(H
);
90 C
= isl_mat_inverse_product(H
, C
);
93 for (i
= 0; i
< B
->n_row
; ++i
) {
94 if (!isl_int_is_divisible_by(C
->row
[1+i
][0], C
->row
[0][0]))
96 isl_int_divexact(C
->row
[1+i
][0], C
->row
[1+i
][0], C
->row
[0][0]);
99 cst
= isl_mat_alloc(B
->ctx
, B
->n_row
, 0);
101 cst
= isl_mat_sub_alloc(C
, 1, B
->n_row
, 0, 1);
102 T
= isl_mat_sub_alloc(U
, B
->n_row
, B
->n_col
- 1, 0, B
->n_row
);
103 cst
= isl_mat_product(T
, cst
);
115 /* Compute and return the matrix
117 * U_1^{-1} diag(d_1, 1, ..., 1)
119 * with U_1 the unimodular completion of the first (and only) row of B.
120 * The columns of this matrix generate the lattice that satisfies
121 * the single (linear) modulo constraint.
123 static struct isl_mat
*parameter_compression_1(
124 struct isl_mat
*B
, struct isl_vec
*d
)
128 U
= isl_mat_alloc(B
->ctx
, B
->n_col
- 1, B
->n_col
- 1);
131 isl_seq_cpy(U
->row
[0], B
->row
[0] + 1, B
->n_col
- 1);
132 U
= isl_mat_unimodular_complete(U
, 1);
133 U
= isl_mat_right_inverse(U
);
136 isl_mat_col_mul(U
, 0, d
->block
.data
[0], 0);
137 U
= isl_mat_lin_to_aff(U
);
141 /* Compute a common lattice of solutions to the linear modulo
142 * constraints specified by B and d.
143 * See also the documentation of isl_mat_parameter_compression.
146 * A = [ L_1^{-T} L_2^{-T} ... L_k^{-T} ]
148 * on a common denominator. This denominator D is the lcm of modulos d.
149 * Since L_i = U_i^{-1} diag(d_i, 1, ... 1), we have
150 * L_i^{-T} = U_i^T diag(d_i, 1, ... 1)^{-T} = U_i^T diag(1/d_i, 1, ..., 1).
151 * Putting this on the common denominator, we have
152 * D * L_i^{-T} = U_i^T diag(D/d_i, D, ..., D).
154 static struct isl_mat
*parameter_compression_multi(
155 struct isl_mat
*B
, struct isl_vec
*d
)
159 struct isl_mat
*A
= NULL
, *U
= NULL
;
168 A
= isl_mat_alloc(B
->ctx
, size
, B
->n_row
* size
);
169 U
= isl_mat_alloc(B
->ctx
, size
, size
);
172 for (i
= 0; i
< B
->n_row
; ++i
) {
173 isl_seq_cpy(U
->row
[0], B
->row
[i
] + 1, size
);
174 U
= isl_mat_unimodular_complete(U
, 1);
177 isl_int_divexact(D
, D
, d
->block
.data
[i
]);
178 for (k
= 0; k
< U
->n_col
; ++k
)
179 isl_int_mul(A
->row
[k
][i
*size
+0], D
, U
->row
[0][k
]);
180 isl_int_mul(D
, D
, d
->block
.data
[i
]);
181 for (j
= 1; j
< U
->n_row
; ++j
)
182 for (k
= 0; k
< U
->n_col
; ++k
)
183 isl_int_mul(A
->row
[k
][i
*size
+j
],
186 A
= isl_mat_left_hermite(A
, 0, NULL
, NULL
);
187 T
= isl_mat_sub_alloc(A
, 0, A
->n_row
, 0, A
->n_row
);
188 T
= isl_mat_lin_to_aff(T
);
191 isl_int_set(T
->row
[0][0], D
);
192 T
= isl_mat_right_inverse(T
);
195 isl_assert(T
->ctx
, isl_int_is_one(T
->row
[0][0]), goto error
);
196 T
= isl_mat_transpose(T
);
209 /* Given a set of modulo constraints
213 * this function returns an affine transformation T,
217 * that bijectively maps the integer vectors y' to integer
218 * vectors y that satisfy the modulo constraints.
220 * This function is inspired by Section 2.5.3
221 * of B. Meister, "Stating and Manipulating Periodicity in the Polytope
222 * Model. Applications to Program Analysis and Optimization".
223 * However, the implementation only follows the algorithm of that
224 * section for computing a particular solution and not for computing
225 * a general homogeneous solution. The latter is incomplete and
226 * may remove some valid solutions.
227 * Instead, we use an adaptation of the algorithm in Section 7 of
228 * B. Meister, S. Verdoolaege, "Polynomial Approximations in the Polytope
229 * Model: Bringing the Power of Quasi-Polynomials to the Masses".
231 * The input is given as a matrix B = [ c A ] and a vector d.
232 * Each element of the vector d corresponds to a row in B.
233 * The output is a lower triangular matrix.
234 * If no integer vector y satisfies the given constraints then
235 * a matrix with zero columns is returned.
237 * We first compute a particular solution y_0 to the given set of
238 * modulo constraints in particular_solution. If no such solution
239 * exists, then we return a zero-columned transformation matrix.
240 * Otherwise, we compute the generic solution to
244 * That is we want to compute G such that
248 * with y'' integer, describes the set of solutions.
250 * We first remove the common factors of each row.
251 * In particular if gcd(A_i,d_i) != 1, then we divide the whole
252 * row i (including d_i) by this common factor. If afterwards gcd(A_i) != 1,
253 * then we divide this row of A by the common factor, unless gcd(A_i) = 0.
254 * In the later case, we simply drop the row (in both A and d).
256 * If there are no rows left in A, then G is the identity matrix. Otherwise,
257 * for each row i, we now determine the lattice of integer vectors
258 * that satisfies this row. Let U_i be the unimodular extension of the
259 * row A_i. This unimodular extension exists because gcd(A_i) = 1.
260 * The first component of
264 * needs to be a multiple of d_i. Let y' = diag(d_i, 1, ..., 1) y''.
267 * y = U_i^{-1} diag(d_i, 1, ..., 1) y''
269 * for arbitrary integer vectors y''. That is, y belongs to the lattice
270 * generated by the columns of L_i = U_i^{-1} diag(d_i, 1, ..., 1).
271 * If there is only one row, then G = L_1.
273 * If there is more than one row left, we need to compute the intersection
274 * of the lattices. That is, we need to compute an L such that
276 * L = L_i L_i' for all i
278 * with L_i' some integer matrices. Let A be constructed as follows
280 * A = [ L_1^{-T} L_2^{-T} ... L_k^{-T} ]
282 * and computed the Hermite Normal Form of A = [ H 0 ] U
285 * L_i^{-T} = H U_{1,i}
289 * H^{-T} = L_i U_{1,i}^T
291 * In other words G = L = H^{-T}.
292 * To ensure that G is lower triangular, we compute and use its Hermite
295 * The affine transformation matrix returned is then
300 * as any y = y_0 + G y' with y' integer is a solution to the original
301 * modulo constraints.
303 struct isl_mat
*isl_mat_parameter_compression(
304 struct isl_mat
*B
, struct isl_vec
*d
)
307 struct isl_mat
*cst
= NULL
;
308 struct isl_mat
*T
= NULL
;
313 isl_assert(B
->ctx
, B
->n_row
== d
->size
, goto error
);
314 cst
= particular_solution(B
, d
);
317 if (cst
->n_col
== 0) {
318 T
= isl_mat_alloc(B
->ctx
, B
->n_col
, 0);
325 /* Replace a*g*row = 0 mod g*m by row = 0 mod m */
326 for (i
= 0; i
< B
->n_row
; ++i
) {
327 isl_seq_gcd(B
->row
[i
] + 1, B
->n_col
- 1, &D
);
328 if (isl_int_is_one(D
))
330 if (isl_int_is_zero(D
)) {
331 B
= isl_mat_drop_rows(B
, i
, 1);
335 isl_seq_cpy(d
->block
.data
+i
, d
->block
.data
+i
+1,
344 isl_seq_scale_down(B
->row
[i
] + 1, B
->row
[i
] + 1, D
, B
->n_col
-1);
345 isl_int_gcd(D
, D
, d
->block
.data
[i
]);
349 isl_int_divexact(d
->block
.data
[i
], d
->block
.data
[i
], D
);
353 T
= isl_mat_identity(B
->ctx
, B
->n_col
);
354 else if (B
->n_row
== 1)
355 T
= parameter_compression_1(B
, d
);
357 T
= parameter_compression_multi(B
, d
);
358 T
= isl_mat_left_hermite(T
, 0, NULL
, NULL
);
361 isl_mat_sub_copy(T
->ctx
, T
->row
+ 1, cst
->row
, cst
->n_row
, 0, 0, 1);
375 /* Given a set of equalities
379 * this function computes a unimodular transformation from a lower-dimensional
380 * space to the original space that bijectively maps the integer points x'
381 * in the lower-dimensional space to the integer points x in the original
382 * space that satisfy the equalities.
384 * The input is given as a matrix B = [ -c M ] and the output is a
385 * matrix that maps [1 x'] to [1 x].
386 * If T2 is not NULL, then *T2 is set to a matrix mapping [1 x] to [1 x'].
388 * First compute the (left) Hermite normal form of M,
390 * M [U1 U2] = M U = H = [H1 0]
392 * M = H Q = [H1 0] [Q1]
395 * with U, Q unimodular, Q = U^{-1} (and H lower triangular).
396 * Define the transformed variables as
398 * x = [U1 U2] [ x1' ] = [U1 U2] [Q1] x
401 * The equalities then become
403 * H1 x1' - c = 0 or x1' = H1^{-1} c = c'
405 * If any of the c' is non-integer, then the original set has no
406 * integer solutions (since the x' are a unimodular transformation
407 * of the x) and a zero-column matrix is returned.
408 * Otherwise, the transformation is given by
410 * x = U1 H1^{-1} c + U2 x2'
412 * The inverse transformation is simply
416 __isl_give isl_mat
*isl_mat_variable_compression(__isl_take isl_mat
*B
,
417 __isl_give isl_mat
**T2
)
420 struct isl_mat
*H
= NULL
, *C
= NULL
, *H1
, *U
= NULL
, *U1
, *U2
, *TC
;
429 H
= isl_mat_sub_alloc(B
, 0, B
->n_row
, 1, dim
);
430 H
= isl_mat_left_hermite(H
, 0, &U
, T2
);
431 if (!H
|| !U
|| (T2
&& !*T2
))
434 *T2
= isl_mat_drop_rows(*T2
, 0, B
->n_row
);
435 *T2
= isl_mat_lin_to_aff(*T2
);
439 C
= isl_mat_alloc(B
->ctx
, 1+B
->n_row
, 1);
442 isl_int_set_si(C
->row
[0][0], 1);
443 isl_mat_sub_neg(C
->ctx
, C
->row
+1, B
->row
, B
->n_row
, 0, 0, 1);
444 H1
= isl_mat_sub_alloc(H
, 0, H
->n_row
, 0, H
->n_row
);
445 H1
= isl_mat_lin_to_aff(H1
);
446 TC
= isl_mat_inverse_product(H1
, C
);
450 if (!isl_int_is_one(TC
->row
[0][0])) {
451 for (i
= 0; i
< B
->n_row
; ++i
) {
452 if (!isl_int_is_divisible_by(TC
->row
[1+i
][0], TC
->row
[0][0])) {
453 struct isl_ctx
*ctx
= B
->ctx
;
461 return isl_mat_alloc(ctx
, 1 + dim
, 0);
463 isl_seq_scale_down(TC
->row
[1+i
], TC
->row
[1+i
], TC
->row
[0][0], 1);
465 isl_int_set_si(TC
->row
[0][0], 1);
467 U1
= isl_mat_sub_alloc(U
, 0, U
->n_row
, 0, B
->n_row
);
468 U1
= isl_mat_lin_to_aff(U1
);
469 U2
= isl_mat_sub_alloc(U
, 0, U
->n_row
, B
->n_row
, U
->n_row
- B
->n_row
);
470 U2
= isl_mat_lin_to_aff(U2
);
472 TC
= isl_mat_product(U1
, TC
);
473 TC
= isl_mat_aff_direct_sum(TC
, U2
);
489 /* Use the n equalities of bset to unimodularly transform the
490 * variables x such that n transformed variables x1' have a constant value
491 * and rewrite the constraints of bset in terms of the remaining
492 * transformed variables x2'. The matrix pointed to by T maps
493 * the new variables x2' back to the original variables x, while T2
494 * maps the original variables to the new variables.
496 static struct isl_basic_set
*compress_variables(
497 struct isl_basic_set
*bset
, struct isl_mat
**T
, struct isl_mat
**T2
)
499 struct isl_mat
*B
, *TC
;
508 isl_assert(bset
->ctx
, isl_basic_set_n_param(bset
) == 0, goto error
);
509 isl_assert(bset
->ctx
, bset
->n_div
== 0, goto error
);
510 dim
= isl_basic_set_n_dim(bset
);
511 isl_assert(bset
->ctx
, bset
->n_eq
<= dim
, goto error
);
515 B
= isl_mat_sub_alloc6(bset
->ctx
, bset
->eq
, 0, bset
->n_eq
, 0, 1 + dim
);
516 TC
= isl_mat_variable_compression(B
, T2
);
519 if (TC
->n_col
== 0) {
525 return isl_basic_set_set_to_empty(bset
);
528 bset
= isl_basic_set_preimage(bset
, T
? isl_mat_copy(TC
) : TC
);
533 isl_basic_set_free(bset
);
537 struct isl_basic_set
*isl_basic_set_remove_equalities(
538 struct isl_basic_set
*bset
, struct isl_mat
**T
, struct isl_mat
**T2
)
546 isl_assert(bset
->ctx
, isl_basic_set_n_param(bset
) == 0, goto error
);
547 bset
= isl_basic_set_gauss(bset
, NULL
);
548 if (ISL_F_ISSET(bset
, ISL_BASIC_SET_EMPTY
))
550 bset
= compress_variables(bset
, T
, T2
);
553 isl_basic_set_free(bset
);
558 /* Check if dimension dim belongs to a residue class
559 * i_dim \equiv r mod m
560 * with m != 1 and if so return m in *modulo and r in *residue.
561 * As a special case, when i_dim has a fixed value v, then
562 * *modulo is set to 0 and *residue to v.
564 * If i_dim does not belong to such a residue class, then *modulo
565 * is set to 1 and *residue is set to 0.
567 int isl_basic_set_dim_residue_class(struct isl_basic_set
*bset
,
568 int pos
, isl_int
*modulo
, isl_int
*residue
)
571 struct isl_mat
*H
= NULL
, *U
= NULL
, *C
, *H1
, *U1
;
575 if (!bset
|| !modulo
|| !residue
)
578 if (isl_basic_set_plain_dim_is_fixed(bset
, pos
, residue
)) {
579 isl_int_set_si(*modulo
, 0);
584 total
= isl_basic_set_total_dim(bset
);
585 nparam
= isl_basic_set_n_param(bset
);
586 H
= isl_mat_sub_alloc6(bset
->ctx
, bset
->eq
, 0, bset
->n_eq
, 1, total
);
587 H
= isl_mat_left_hermite(H
, 0, &U
, NULL
);
591 isl_seq_gcd(U
->row
[nparam
+ pos
]+bset
->n_eq
,
592 total
-bset
->n_eq
, modulo
);
593 if (isl_int_is_zero(*modulo
))
594 isl_int_set_si(*modulo
, 1);
595 if (isl_int_is_one(*modulo
)) {
596 isl_int_set_si(*residue
, 0);
602 C
= isl_mat_alloc(bset
->ctx
, 1+bset
->n_eq
, 1);
605 isl_int_set_si(C
->row
[0][0], 1);
606 isl_mat_sub_neg(C
->ctx
, C
->row
+1, bset
->eq
, bset
->n_eq
, 0, 0, 1);
607 H1
= isl_mat_sub_alloc(H
, 0, H
->n_row
, 0, H
->n_row
);
608 H1
= isl_mat_lin_to_aff(H1
);
609 C
= isl_mat_inverse_product(H1
, C
);
611 U1
= isl_mat_sub_alloc(U
, nparam
+pos
, 1, 0, bset
->n_eq
);
612 U1
= isl_mat_lin_to_aff(U1
);
614 C
= isl_mat_product(U1
, C
);
617 if (!isl_int_is_divisible_by(C
->row
[1][0], C
->row
[0][0])) {
618 bset
= isl_basic_set_copy(bset
);
619 bset
= isl_basic_set_set_to_empty(bset
);
620 isl_basic_set_free(bset
);
621 isl_int_set_si(*modulo
, 1);
622 isl_int_set_si(*residue
, 0);
625 isl_int_divexact(*residue
, C
->row
[1][0], C
->row
[0][0]);
626 isl_int_fdiv_r(*residue
, *residue
, *modulo
);
635 /* Check if dimension dim belongs to a residue class
636 * i_dim \equiv r mod m
637 * with m != 1 and if so return m in *modulo and r in *residue.
638 * As a special case, when i_dim has a fixed value v, then
639 * *modulo is set to 0 and *residue to v.
641 * If i_dim does not belong to such a residue class, then *modulo
642 * is set to 1 and *residue is set to 0.
644 int isl_set_dim_residue_class(struct isl_set
*set
,
645 int pos
, isl_int
*modulo
, isl_int
*residue
)
651 if (!set
|| !modulo
|| !residue
)
655 isl_int_set_si(*modulo
, 0);
656 isl_int_set_si(*residue
, 0);
660 if (isl_basic_set_dim_residue_class(set
->p
[0], pos
, modulo
, residue
)<0)
666 if (isl_int_is_one(*modulo
))
672 for (i
= 1; i
< set
->n
; ++i
) {
673 if (isl_basic_set_dim_residue_class(set
->p
[i
], pos
, &m
, &r
) < 0)
675 isl_int_gcd(*modulo
, *modulo
, m
);
676 isl_int_sub(m
, *residue
, r
);
677 isl_int_gcd(*modulo
, *modulo
, m
);
678 if (!isl_int_is_zero(*modulo
))
679 isl_int_fdiv_r(*residue
, *residue
, *modulo
);
680 if (isl_int_is_one(*modulo
))