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Eigen workaround #137

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Merged
michael-petersen merged 4 commits into from
May 9, 2025
Merged

Eigen workaround #137

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8b7c6b7
Fix omitted norm in OrthoFunction::dumpOrtho()
May 1, 2025
15db8b5
Fix code typo in version-toggled Eigen workaround for spherical coeff…
May 4, 2025
4276eda
version bump only [no CI]
michael-petersen May 9, 2025
e55ffe9
Added comments on normalization
May 9, 2025
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2 changes: 1 addition & 1 deletion CMakeLists.txt
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Expand Up @@ -2,7 +2,7 @@ cmake_minimum_required(VERSION 3.25) # Needed for CUDA, MPI, and CTest features

project(
EXP
VERSION "7.8.3"
VERSION "7.8.4"
HOMEPAGE_URL https://github.com/EXP-code/EXP
LANGUAGES C CXX Fortran)

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2 changes: 1 addition & 1 deletion expui/Coefficients.cc
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Expand Up @@ -131,7 +131,7 @@ namespace CoefClasses
in2.transposeInPlace();

for (size_t c=0, n=0; c<in.cols(); c++) {
for (size_t r=0, n=0; r<in.rows(); r++) {
for (size_t r=0; r<in.rows(); r++) {
in(r, c) = in2.data()[n++];
}
}
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15 changes: 10 additions & 5 deletions exputil/OrthoFunction.cc
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Expand Up @@ -54,6 +54,7 @@ double OrthoFunction::scalar_prod(int n, int m)
return ans;
}

// Compute the unnormalized polynomial
Eigen::VectorXd OrthoFunction::poly_eval(double t, int n)
{
Eigen::VectorXd ret(n+1);
Expand All @@ -68,6 +69,8 @@ Eigen::VectorXd OrthoFunction::poly_eval(double t, int n)
return ret;
}

// Compute the orthogonality matrix. A perfect result is the unit
// matrix.
Eigen::MatrixXd OrthoFunction::testOrtho()
{
Eigen::MatrixXd ret(nmax+1, nmax+1);
Expand All @@ -80,10 +83,10 @@ Eigen::MatrixXd OrthoFunction::testOrtho()
double f = dx*lq->weight(i)*d_r_to_x(x)*pow(r, dof-1);
double y = 2.0*r/scale; if (segment) y = x;

// Evaluate polynomial
// Evaluate the unnormalized polynomial
Eigen::VectorXd p = poly_eval(y, nmax) * W(r);

// Compute scalar products
// Compute scalar products with the normalizations
for (int n1=0; n1<=nmax; n1++) {
for (int n2=0; n2<=nmax; n2++) {
ret(n1, n2) += f * p(n1) * p(n2) / sqrt(norm[n1]*norm[n2]);
Expand All @@ -110,11 +113,11 @@ void OrthoFunction::dumpOrtho(const std::string& filename)
double r = x_to_r(x);
double y = 2.0*r/scale; if (segment) y = x;

// Evaluate polynomial
// Evaluate polynomial and apply the normalization
//
Eigen::VectorXd p = poly_eval(y, nmax) * W(r);
fout << std::setw(16) << r;
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Copilot AI May 9, 2025

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[nitpick] Consider adding a comment explaining the rationale behind dividing by sqrt(norm[n]) to ensure that future maintainers understand the fix for the spherical coefficient read.

Suggested change
fout << std::setw(16) << r;
fout << std::setw(16) << r;
// Normalize the polynomial coefficients by dividing by sqrt(norm[n])
// to ensure they adhere to the expected orthogonality or normalization properties.

Copilot uses AI. Check for mistakes.
for (int n=0; n<nmax; n++) fout << std::setw(16) << p(n);
for (int n=0; n<nmax; n++) fout << std::setw(16) << p(n)/sqrt(norm[n]);
fout << std::endl;
}
}
Expand All @@ -132,9 +135,11 @@ Eigen::VectorXd OrthoFunction::operator()(double r)
double y = 2.0*r/scale;
if (segment) y = r_to_x(r);

// Generate normalized orthogonal functions
// Generate normalized orthogonal functions with weighting
auto p = poly_eval(y, nmax);
for (int i=0; i<=nmax; i++) p(i) *= w/sqrt(norm[i]);

// The inner product of p(i), p(j) is Kronecker delta(i, j)

return p;
}