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Small simplifications

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Ryan Pavlik 2021-12-23 11:47:50 -06:00 committed by phkahler
parent 8ab70c2c8d
commit 18dc8ee12c
1 changed files with 15 additions and 9 deletions
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24
src/system.cpp
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@ -15,6 +15,8 @@
// always be much less than LENGTH_EPS, and in practice should be much less.
const double System::CONVERGE_TOLERANCE = (LENGTH_EPS/(1e2));
constexpr size_t LikelyPartialCountPerEq = 10;
bool System::WriteJacobian(int tag) {
// Clear all
mat.param.clear();
@ -34,7 +36,7 @@ bool System::WriteJacobian(int tag) {
}
mat.m = mat.eq.size();
mat.A.sym.resize(mat.m, mat.n);
mat.A.sym.reserve(Eigen::VectorXi::Constant(mat.n, 10));
mat.A.sym.reserve(Eigen::VectorXi::Constant(mat.n, LikelyPartialCountPerEq));
// Fill the param id to index map
std::map<uint32_t, int> paramToIndex;
@ -46,11 +48,13 @@ bool System::WriteJacobian(int tag) {
return false;
}
std::vector<hParam> paramsUsed;
// A single possibly-too-large allocation is probably preferred here?
// In some experimenting, this is almost always the right size.
// Value is usually between 0 and 20, comes from number of constraints?
mat.B.sym.reserve(mat.eq.size());
for(size_t i = 0; i < mat.eq.size(); i++) {
Equation *e = mat.eq[i];
if(e->tag != tag) continue;
// Simplify (fold) then deep-copy the current equation.
Expr *f = e->e->FoldConstants();
f = f->DeepCopyWithParamsAsPointers(&param, &(SK.param));
@ -58,13 +62,17 @@ bool System::WriteJacobian(int tag) {
f->ParamsUsedList(&paramsUsed);
for(hParam &p : paramsUsed) {
auto j = paramToIndex.find(p.v);
if(j == paramToIndex.end()) continue;
// Find the index of this parameter
auto it = paramToIndex.find(p.v);
if(it == paramToIndex.end()) continue;
// this is the parameter index
const int j = it->second;
// compute partial derivative of f
Expr *pd = f->PartialWrt(p);
pd = pd->FoldConstants();
if(pd->IsZeroConst())
continue;
mat.A.sym.insert(i, j->second) = pd;
mat.A.sym.insert(i, j) = pd;
}
paramsUsed.clear();
mat.B.sym.push_back(f);
@ -248,14 +256,12 @@ bool System::SolveLeastSquares() {
// 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.
mat.scale = VectorXd(mat.n);
mat.scale = VectorXd::Ones(mat.n);
for(int 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;
mat.scale[c] = 1 / 20.0;
}
}