375 lines
10 KiB
C++
375 lines
10 KiB
C++
#include "solvespace.h"
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void System::WriteJacobian(int eqTag, int paramTag) {
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int a, i, j;
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j = 0;
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for(a = 0; a < param.n; a++) {
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Param *p = &(param.elem[a]);
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if(p->tag != paramTag) continue;
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mat.param[j] = p->h;
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j++;
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}
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mat.n = j;
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i = 0;
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for(a = 0; a < eq.n; a++) {
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Equation *e = &(eq.elem[a]);
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if(e->tag != eqTag) continue;
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mat.eq[i] = e->h;
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Expr *f = e->e->DeepCopyWithParamsAsPointers(¶m, &(SS.param));
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f = f->FoldConstants();
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// Hash table (31 bits) to accelerate generation of zero partials.
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DWORD scoreboard = f->ParamsUsed();
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for(j = 0; j < mat.n; j++) {
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Expr *pd;
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if(scoreboard & (1 << (mat.param[j].v % 31))) {
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pd = f->PartialWrt(mat.param[j]);
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pd = pd->FoldConstants();
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} else {
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pd = Expr::FromConstant(0);
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}
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mat.A.sym[i][j] = pd;
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}
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mat.B.sym[i] = f;
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i++;
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}
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mat.m = i;
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}
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void System::EvalJacobian(void) {
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int i, j;
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for(i = 0; i < mat.m; i++) {
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for(j = 0; j < mat.n; j++) {
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mat.A.num[i][j] = (mat.A.sym[i][j])->Eval();
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}
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}
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}
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void System::MarkAsDragged(hParam p) {
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int j;
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for(j = 0; j < mat.n; j++) {
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if(mat.param[j].v == p.v) {
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mat.dragged[j] = true;
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}
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}
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}
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static int BySensitivity(const void *va, const void *vb) {
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const int *a = (const int *)va, *b = (const int *)vb;
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if(SS.sys.mat.dragged[*a] && !SS.sys.mat.dragged[*b]) return 1;
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if(SS.sys.mat.dragged[*b] && !SS.sys.mat.dragged[*a]) return -1;
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double as = SS.sys.mat.sens[*a];
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double bs = SS.sys.mat.sens[*b];
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if(as < bs) return 1;
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if(as > bs) return -1;
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return 0;
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}
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void System::SortBySensitivity(void) {
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// For each unknown, sum up the sensitivities in that column of the
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// Jacobian
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int i, j;
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for(j = 0; j < mat.n; j++) {
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double s = 0;
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int i;
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for(i = 0; i < mat.m; i++) {
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s += fabs(mat.A.num[i][j]);
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}
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mat.sens[j] = s;
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}
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for(j = 0; j < mat.n; j++) {
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mat.dragged[j] = false;
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mat.permutation[j] = j;
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}
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if(SS.GW.pending.point.v) {
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Entity *p = SS.entity.FindByIdNoOops(SS.GW.pending.point);
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// If we're solving an earlier group, then the pending point might
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// not exist in the entity tables yet.
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if(p) {
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switch(p->type) {
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case Entity::POINT_XFRMD:
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case Entity::POINT_IN_3D:
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MarkAsDragged(p->param[0]);
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MarkAsDragged(p->param[1]);
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MarkAsDragged(p->param[2]);
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break;
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case Entity::POINT_IN_2D:
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MarkAsDragged(p->param[0]);
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MarkAsDragged(p->param[1]);
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break;
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}
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}
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}
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qsort(mat.permutation, mat.n, sizeof(mat.permutation[0]), BySensitivity);
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int origPos[MAX_UNKNOWNS];
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int entryWithOrigPos[MAX_UNKNOWNS];
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for(j = 0; j < mat.n; j++) {
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origPos[j] = j;
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entryWithOrigPos[j] = j;
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}
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#define SWAP(T, a, b) do { T temp = (a); (a) = (b); (b) = temp; } while(0)
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for(j = 0; j < mat.n; j++) {
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int dest = j; // we are writing to position j
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// And the source is whichever position ahead of us can be swapped
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// in to make the permutation vectors line up.
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int src = entryWithOrigPos[mat.permutation[j]];
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for(i = 0; i < mat.m; i++) {
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SWAP(double, mat.A.num[i][src], mat.A.num[i][dest]);
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SWAP(Expr *, mat.A.sym[i][src], mat.A.sym[i][dest]);
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}
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SWAP(hParam, mat.param[src], mat.param[dest]);
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SWAP(int, origPos[src], origPos[dest]);
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if(mat.permutation[dest] != origPos[dest]) oops();
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// Update the table; only necessary to do this for src, since dest
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// is already done.
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entryWithOrigPos[origPos[src]] = src;
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}
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}
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bool System::Tol(double v) {
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return (fabs(v) < 0.001);
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}
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void System::GaussJordan(void) {
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int i, j;
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for(j = 0; j < mat.n; j++) {
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mat.bound[j] = false;
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}
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// Now eliminate.
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i = 0;
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for(j = 0; j < mat.n; j++) {
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// First, seek a pivot in our column.
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int ip, imax;
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double max = 0;
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for(ip = i; ip < mat.m; ip++) {
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double v = fabs(mat.A.num[ip][j]);
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if(v > max) {
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imax = ip;
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max = v;
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}
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}
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if(!Tol(max)) {
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// There's a usable pivot in this column. Swap it in:
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int js;
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for(js = j; js < mat.n; js++) {
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double temp;
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temp = mat.A.num[imax][js];
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mat.A.num[imax][js] = mat.A.num[i][js];
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mat.A.num[i][js] = temp;
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}
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// Get a 1 as the leading entry in the row we're working on.
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double v = mat.A.num[i][j];
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for(js = 0; js < mat.n; js++) {
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mat.A.num[i][js] /= v;
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}
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// Eliminate this column from rows except this one.
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int is;
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for(is = 0; is < mat.m; is++) {
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if(is == i) continue;
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// We're trying to drive A[is][j] to zero. We know
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// that A[i][j] is 1, so we want to subtract off
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// A[is][j] times our present row.
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double v = mat.A.num[is][j];
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for(js = 0; js < mat.n; js++) {
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mat.A.num[is][js] -= v*mat.A.num[i][js];
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}
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mat.A.num[is][j] = 0;
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}
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// And mark this as a bound variable.
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mat.bound[j] = true;
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// Move on to the next row, since we just used this one to
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// eliminate from column j.
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i++;
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if(i >= mat.m) break;
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}
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}
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}
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bool System::SolveLinearSystem(void) {
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if(mat.m != mat.n) oops();
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// Gaussian elimination, with partial pivoting. It's an error if the
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// matrix is singular, because that means two constraints are
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// equivalent.
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int i, j, ip, jp, imax;
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double max, temp;
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for(i = 0; i < mat.m; i++) {
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// We are trying eliminate the term in column i, for rows i+1 and
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// greater. First, find a pivot (between rows i and N-1).
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max = 0;
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for(ip = i; ip < mat.m; ip++) {
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if(fabs(mat.A.num[ip][i]) > max) {
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imax = ip;
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max = fabs(mat.A.num[ip][i]);
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}
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}
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if(fabs(max) < 1e-12) return false;
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// Swap row imax with row i
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for(jp = 0; jp < mat.n; jp++) {
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temp = mat.A.num[i][jp];
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mat.A.num[i][jp] = mat.A.num[imax][jp];
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mat.A.num[imax][jp] = temp;
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}
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temp = mat.B.num[i];
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mat.B.num[i] = mat.B.num[imax];
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mat.B.num[imax] = temp;
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// For rows i+1 and greater, eliminate the term in column i.
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for(ip = i+1; ip < mat.m; ip++) {
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temp = mat.A.num[ip][i]/mat.A.num[i][i];
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for(jp = 0; jp < mat.n; jp++) {
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mat.A.num[ip][jp] -= temp*(mat.A.num[i][jp]);
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}
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mat.B.num[ip] -= temp*mat.B.num[i];
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}
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}
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// We've put the matrix in upper triangular form, so at this point we
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// can solve by back-substitution.
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for(i = mat.m - 1; i >= 0; i--) {
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if(fabs(mat.A.num[i][i]) < 1e-10) return false;
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temp = mat.B.num[i];
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for(j = mat.n - 1; j > i; j--) {
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temp -= mat.X[j]*mat.A.num[i][j];
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}
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mat.X[i] = temp / mat.A.num[i][i];
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}
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return true;
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}
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bool System::NewtonSolve(int tag) {
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WriteJacobian(tag, tag);
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if(mat.m != mat.n) oops();
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int iter = 0;
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bool converged = false;
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int i;
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// Evaluate the functions at our operating point.
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for(i = 0; i < mat.m; i++) {
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mat.B.num[i] = (mat.B.sym[i])->Eval();
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}
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do {
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// And evaluate the Jacobian at our initial operating point.
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EvalJacobian();
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if(!SolveLinearSystem()) break;
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// Take the Newton step;
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// J(x_n) (x_{n+1} - x_n) = 0 - F(x_n)
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for(i = 0; i < mat.m; i++) {
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(param.FindById(mat.param[i]))->val -= mat.X[i];
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}
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// Re-evalute the functions, since the params have just changed.
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for(i = 0; i < mat.m; i++) {
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mat.B.num[i] = (mat.B.sym[i])->Eval();
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}
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// Check for convergence
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converged = true;
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for(i = 0; i < mat.m; i++) {
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if(!Tol(mat.B.num[i])) {
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converged = false;
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break;
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}
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}
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} while(iter++ < 50 && !converged);
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if(converged) {
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return true;
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} else {
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dbp("no convergence");
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return false;
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}
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}
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bool System::Solve(void) {
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int i, j;
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/*
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dbp("%d equations", eq.n);
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for(i = 0; i < eq.n; i++) {
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dbp(" %.3f = %s = 0", eq.elem[i].e->Eval(), eq.elem[i].e->Print());
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}
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dbp("%d parameters", param.n); */
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param.ClearTags();
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eq.ClearTags();
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WriteJacobian(0, 0);
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EvalJacobian();
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SortBySensitivity();
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/* dbp("write/eval jacboian=%d", GetMilliseconds() - in);
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for(i = 0; i < mat.m; i++) {
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dbp("function %d: %s", i, mat.B.sym[i]->Print());
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}
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dbp("m=%d", mat.m);
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for(i = 0; i < mat.m; i++) {
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for(j = 0; j < mat.n; j++) {
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dbp("A[%d][%d] = %.3f", i, j, mat.A.num[i][j]);
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}
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} */
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GaussJordan();
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/* dbp("bound states:");
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for(j = 0; j < mat.n; j++) {
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dbp(" param %08x: %d", mat.param[j], mat.bound[j]);
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} */
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// Fix any still-free variables wherever they are now.
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for(j = mat.n-1; j >= 0; --j) {
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if(mat.bound[j]) continue;
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Param *p = param.FindByIdNoOops(mat.param[j]);
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if(!p) {
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// This is parameter does not occur in this group, so it's
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// not available to assume.
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continue;
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}
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p->tag = ASSUMED;
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}
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bool ok = NewtonSolve(0);
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if(ok) {
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// System solved correctly, so write the new values back in to the
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// main parameter table.
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for(i = 0; i < param.n; i++) {
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Param *p = &(param.elem[i]);
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Param *pp = SS.GetParam(p->h);
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pp->val = p->val;
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pp->known = true;
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// The main param table keeps track of what was assumed, to
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// choose which point to drag so that it actually moves.
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pp->assumed = (p->tag == ASSUMED);
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}
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}
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return true;
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}
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