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