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solvespace/src/system.cpp

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//-----------------------------------------------------------------------------
// Once we've written our constraint equations in the symbolic algebra system,
// these routines linearize them, and solve by a modified Newton's method.
// This also contains the routines to detect non-convergence or inconsistency,
// and report diagnostics to the user.
//
// Copyright 2008-2013 Jonathan Westhues.
//-----------------------------------------------------------------------------
#include "solvespace.h"
// This tolerance is used to determine whether two (linearized) constraints
// are linearly dependent. If this is too small, then we will attempt to
// solve truly inconsistent systems and fail. But if it's too large, then
// we will give up on legitimate systems like a skinny right angle triangle by
// its hypotenuse and long side.
const double System::RANK_MAG_TOLERANCE = 1e-4;
// The solver will converge all unknowns to within this tolerance. This must
// always be much less than LENGTH_EPS, and in practice should be much less.
const double System::CONVERGE_TOLERANCE = (LENGTH_EPS/(1e2));
bool System::WriteJacobian(int tag) {
int j = 0;
for(auto &p : param) {
if(j >= MAX_UNKNOWNS)
return false;
if(p.tag != tag)
continue;
mat.param[j] = p.h;
j++;
}
mat.n = j;
int i = 0;
for(auto &e : eq) {
if(i >= MAX_UNKNOWNS) return false;
if(e.tag != tag)
continue;
mat.eq[i] = e.h;
Expr *f = e.e->DeepCopyWithParamsAsPointers(&param, &(SK.param));
f = f->FoldConstants();
// Hash table (61 bits) to accelerate generation of zero partials.
uint64_t scoreboard = f->ParamsUsed();
for(j = 0; j < mat.n; j++) {
Expr *pd;
if(scoreboard & ((uint64_t)1 << (mat.param[j].v % 61)) &&
f->DependsOn(mat.param[j]))
{
pd = f->PartialWrt(mat.param[j]);
pd = pd->FoldConstants();
pd = pd->DeepCopyWithParamsAsPointers(&param, &(SK.param));
} else {
pd = Expr::From(0.0);
}
mat.A.sym[i][j] = pd;
}
mat.B.sym[i] = f;
i++;
}
mat.m = i;
return true;
}
void System::EvalJacobian() {
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) {
hParam *pp;
for(pp = dragged.First(); pp; pp = dragged.NextAfter(pp)) {
if(p == *pp) return true;
}
return false;
}
void System::SolveBySubstitution() {
for(auto &teq : eq) {
Expr *tex = teq.e;
if(tex->op == Expr::Op::MINUS &&
tex->a->op == Expr::Op::PARAM &&
tex->b->op == Expr::Op::PARAM)
{
hParam a = tex->a->parh;
hParam b = tex->b->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
std::swap(a, b);
}
for(auto &req : eq) {
req.e->Substitute(a, b); // A becomes B, B unchanged
}
for(auto &rp : param) {
if(rp.substd == a) {
rp.substd = b;
}
}
Param *ptr = param.FindById(a);
ptr->tag = VAR_SUBSTITUTED;
ptr->substd = b;
teq.tag = EQ_SUBSTITUTED;
}
}
}
//-----------------------------------------------------------------------------
// Calculate the rank of the Jacobian matrix, by Gram-Schimdt orthogonalization
// in place. A row (~equation) is considered to be all zeros if its magnitude
// is less than the tolerance RANK_MAG_TOLERANCE.
//-----------------------------------------------------------------------------
int System::CalculateRank() {
// Actually work with magnitudes squared, not the magnitudes
double rowMag[MAX_UNKNOWNS] = {};
double tol = RANK_MAG_TOLERANCE*RANK_MAG_TOLERANCE;
int i, iprev, j;
int rank = 0;
for(i = 0; i < mat.m; i++) {
// Subtract off this row's component in the direction of any
// previous rows
for(iprev = 0; iprev < i; iprev++) {
if(rowMag[iprev] <= tol) continue; // ignore zero rows
double dot = 0;
for(j = 0; j < mat.n; j++) {
dot += (mat.A.num[iprev][j]) * (mat.A.num[i][j]);
}
for(j = 0; j < mat.n; j++) {
mat.A.num[i][j] -= (dot/rowMag[iprev])*mat.A.num[iprev][j];
}
}
// Our row is now normal to all previous rows; calculate the
// magnitude of what's left
double mag = 0;
for(j = 0; j < mat.n; j++) {
mag += (mat.A.num[i][j]) * (mat.A.num[i][j]);
}
if(mag > tol) {
rank++;
}
rowMag[i] = mag;
}
return rank;
}
bool System::TestRank(int *rank) {
EvalJacobian();
int jacobianRank = CalculateRank();
if(rank) *rank = jacobianRank;
return jacobianRank == mat.m;
}
bool System::SolveLinearSystem(double X[], double A[][MAX_UNKNOWNS],
double B[], int n)
{
// 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 = 0;
double max, temp;
for(i = 0; i < n; 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 < n; ip++) {
if(ffabs(A[ip][i]) > max) {
imax = ip;
max = ffabs(A[ip][i]);
}
}
// Don't give up on a singular matrix unless it's really bad; the
// assumption code is responsible for identifying that condition,
// so we're not responsible for reporting that error.
if(ffabs(max) < 1e-20) continue;
// Swap row imax with row i
for(jp = 0; jp < n; jp++) {
swap(A[i][jp], A[imax][jp]);
}
swap(B[i], B[imax]);
// For rows i+1 and greater, eliminate the term in column i.
for(ip = i+1; ip < n; ip++) {
temp = A[ip][i]/A[i][i];
for(jp = i; jp < n; jp++) {
A[ip][jp] -= temp*(A[i][jp]);
}
B[ip] -= temp*B[i];
}
}
// We've put the matrix in upper triangular form, so at this point we
// can solve by back-substitution.
for(i = n - 1; i >= 0; i--) {
if(ffabs(A[i][i]) < 1e-20) continue;
temp = B[i];
for(j = n - 1; j > i; j--) {
temp -= X[j]*A[i][j];
}
X[i] = temp / A[i][i];
}
return true;
}
bool System::SolveLeastSquares() {
int r, c, i;
// Scale the columns; this scale weights the parameters for the least
// squares solve, so that we can encourage the solver to make bigger
// changes in some parameters, and smaller in others.
for(c = 0; c < mat.n; c++) {
if(IsDragged(mat.param[c])) {
// It's least squares, so this parameter doesn't need to be all
// that big to get a large effect.
mat.scale[c] = 1/20.0;
} else {
mat.scale[c] = 1;
}
for(r = 0; r < mat.m; r++) {
mat.A.num[r][c] *= mat.scale[c];
}
}
// Write A*A'
for(r = 0; r < mat.m; r++) {
for(c = 0; c < mat.m; c++) { // yes, AAt is square
double sum = 0;
for(i = 0; i < mat.n; i++) {
sum += mat.A.num[r][i]*mat.A.num[c][i];
}
mat.AAt[r][c] = sum;
}
}
if(!SolveLinearSystem(mat.Z, mat.AAt, mat.B.num, mat.m)) return false;
// And multiply that by A' to get our solution.
for(c = 0; c < mat.n; c++) {
double sum = 0;
for(i = 0; i < mat.m; i++) {
sum += mat.A.num[i][c]*mat.Z[i];
}
mat.X[c] = sum * mat.scale[c];
}
return true;
}
bool System::NewtonSolve(int tag) {
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(!SolveLeastSquares()) break;
// Take the Newton step;
// J(x_n) (x_{n+1} - x_n) = 0 - F(x_n)
for(i = 0; i < mat.n; i++) {
Param *p = param.FindById(mat.param[i]);
p->val -= mat.X[i];
if(isnan(p->val)) {
// Very bad, and clearly not convergent
return false;
}
}
// 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(isnan(mat.B.num[i])) {
return false;
}
if(ffabs(mat.B.num[i]) > CONVERGE_TOLERANCE) {
converged = false;
break;
}
}
} while(iter++ < 50 && !converged);
return converged;
}
void System::WriteEquationsExceptFor(hConstraint hc, Group *g) {
// Generate all the equations from constraints in this group
for(auto &con : SK.constraint) {
ConstraintBase *c = &con;
if(c->group != g->h) continue;
if(c->h == hc) continue;
if(c->HasLabel() && c->type != Constraint::Type::COMMENT &&
g->allDimsReference)
{
// When all dimensions are reference, we adjust them to display
// the correct value, and then don't generate any equations.
c->ModifyToSatisfy();
continue;
}
if(g->relaxConstraints && c->type != Constraint::Type::POINTS_COINCIDENT) {
// When the constraints are relaxed, we keep only the point-
// coincident constraints, and the constraints generated by
// the entities and groups.
continue;
}
c->GenerateEquations(&eq);
}
// And the equations from entities
for(auto &ent : SK.entity) {
EntityBase *e = &ent;
if(e->group != g->h) continue;
e->GenerateEquations(&eq);
}
// And from the groups themselves
g->GenerateEquations(&eq);
}
void System::FindWhichToRemoveToFixJacobian(Group *g, List<hConstraint> *bad, bool forceDofCheck) {
int a;
for(a = 0; a < 2; a++) {
for(auto &con : SK.constraint) {
ConstraintBase *c = &con;
if(c->group != g->h) continue;
if((c->type == Constraint::Type::POINTS_COINCIDENT && a == 0) ||
(c->type != Constraint::Type::POINTS_COINCIDENT && a == 1))
{
// Do the constraints in two passes: first everything but
// the point-coincident constraints, then only those
// constraints (so they appear last in the list).
continue;
}
param.ClearTags();
eq.Clear();
WriteEquationsExceptFor(c->h, g);
eq.ClearTags();
// It's a major speedup to solve the easy ones by substitution here,
// and that doesn't break anything.
if(!forceDofCheck) {
SolveBySubstitution();
}
WriteJacobian(0);
EvalJacobian();
int rank = CalculateRank();
if(rank == mat.m) {
// We fixed it by removing this constraint
bad->Add(&(c->h));
}
}
}
}
SolveResult System::Solve(Group *g, int *rank, int *dof, List<hConstraint> *bad,
bool andFindBad, bool andFindFree, bool forceDofCheck)
{
WriteEquationsExceptFor(Constraint::NO_CONSTRAINT, g);
int i;
bool rankOk;
/*
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);
} */
// All params and equations are assigned to group zero.
param.ClearTags();
eq.ClearTags();
// Solving by substitution eliminates duplicate e.g. H/V constraints, which can cause rank test
// to succeed even on overdefined systems, which will fail later.
if(!forceDofCheck) {
SolveBySubstitution();
}
// Before solving the big system, see if we can find any equations that
// are soluble alone. This can be a huge speedup. We don't know whether
// the system is consistent yet, but if it isn't then we'll catch that
// later.
int alone = 1;
for(auto &e : eq) {
if(e.tag != 0)
continue;
hParam hp = e.e->ReferencedParams(&param);
if(hp == Expr::NO_PARAMS) continue;
if(hp == Expr::MULTIPLE_PARAMS) continue;
Param *p = param.FindById(hp);
if(p->tag != 0) continue; // let rank test catch inconsistency
e.tag = alone;
p->tag = alone;
WriteJacobian(alone);
if(!NewtonSolve(alone)) {
// We don't do the rank test, so let's arbitrarily return
// the DIDNT_CONVERGE result here.
rankOk = true;
// Failed to converge, bail out early
goto didnt_converge;
}
alone++;
}
// Now write the Jacobian for what's left, and do a rank test; that
// tells us if the system is inconsistently constrained.
if(!WriteJacobian(0)) {
return SolveResult::TOO_MANY_UNKNOWNS;
}
rankOk = TestRank(rank);
// And do the leftovers as one big system
if(!NewtonSolve(0)) {
goto didnt_converge;
}
rankOk = TestRank(rank);
if(!rankOk) {
if(andFindBad) FindWhichToRemoveToFixJacobian(g, bad, forceDofCheck);
} else {
// This is not the full Jacobian, but any substitutions or single-eq
// solves removed one equation and one unknown, therefore no effect
// on the number of DOF.
if(dof) *dof = CalculateDof();
MarkParamsFree(andFindFree);
}
// System solved correctly, so write the new values back in to the
// main parameter table.
for(auto &p : param) {
double val;
if(p.tag == VAR_SUBSTITUTED) {
val = param.FindById(p.substd)->val;
} else {
val = p.val;
}
Param *pp = SK.GetParam(p.h);
pp->val = val;
pp->known = true;
pp->free = p.free;
}
return rankOk ? SolveResult::OKAY : SolveResult::REDUNDANT_OKAY;
didnt_converge:
SK.constraint.ClearTags();
// Not using range-for here because index is used in additional ways
for(i = 0; i < eq.n; i++) {
if(ffabs(mat.B.num[i]) > CONVERGE_TOLERANCE || isnan(mat.B.num[i])) {
// This constraint is unsatisfied.
if(!mat.eq[i].isFromConstraint()) continue;
hConstraint hc = mat.eq[i].constraint();
ConstraintBase *c = SK.constraint.FindByIdNoOops(hc);
if(!c) continue;
// Don't double-show constraints that generated multiple
// unsatisfied equations
if(!c->tag) {
bad->Add(&(c->h));
c->tag = 1;
}
}
}
return rankOk ? SolveResult::DIDNT_CONVERGE : SolveResult::REDUNDANT_DIDNT_CONVERGE;
}
SolveResult System::SolveRank(Group *g, int *rank, int *dof, List<hConstraint> *bad,
bool andFindBad, bool andFindFree)
{
WriteEquationsExceptFor(Constraint::NO_CONSTRAINT, g);
// All params and equations are assigned to group zero.
param.ClearTags();
eq.ClearTags();
// Now write the Jacobian, and do a rank test; that
// tells us if the system is inconsistently constrained.
if(!WriteJacobian(0)) {
return SolveResult::TOO_MANY_UNKNOWNS;
}
bool rankOk = TestRank(rank);
if(!rankOk) {
if(andFindBad) FindWhichToRemoveToFixJacobian(g, bad, /*forceDofCheck=*/true);
} else {
if(dof) *dof = CalculateDof();
MarkParamsFree(andFindFree);
}
return rankOk ? SolveResult::OKAY : SolveResult::REDUNDANT_OKAY;
}
void System::Clear() {
entity.Clear();
param.Clear();
eq.Clear();
dragged.Clear();
}
void System::MarkParamsFree(bool find) {
// If requested, find all the free (unbound) variables. This might be
// more than the number of degrees of freedom. Don't always do this,
// because the display would get annoying and it's slow.
for(auto &p : param) {
p.free = false;
if(find) {
if(p.tag == 0) {
p.tag = VAR_DOF_TEST;
WriteJacobian(0);
EvalJacobian();
int rank = CalculateRank();
if(rank == mat.m) {
p.free = true;
}
p.tag = 0;
}
}
}
}
int System::CalculateDof() {
return mat.n - mat.m;
}
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