From 1423c98e8a61fa036fc9a15e46565485c9e6e62c Mon Sep 17 00:00:00 2001
From: Jaron Kent-Dobias <jaron@kent-dobias.com>
Date: Sun, 21 Apr 2024 15:38:54 +0200
Subject: Renamed some functions and variables.
---
least_squares.cpp | 40 ++++++++++++++++++++--------------------
1 file changed, 20 insertions(+), 20 deletions(-)
diff --git a/least_squares.cpp b/least_squares.cpp
index faf4ee7..00993db 100644
--- a/least_squares.cpp
+++ b/least_squares.cpp
@@ -43,7 +43,7 @@ Real HFromV(const Vector& V) {
return 0.5 * V.squaredNorm();
}
-Vector dHFromVdV(const Vector& V, const Matrix& ∂V) {
+Vector ∂HFromV∂V(const Vector& V, const Matrix& ∂V) {
return V.transpose() * ∂V;
}
@@ -69,7 +69,7 @@ private:
auto [V, ∂V, ∂∂V] = V_∂V_∂∂V(x);
Real H = HFromV(V);
- Vector ∂H = dHFromVdV(V, ∂V);
+ Vector ∂H = ∂HFromV∂V(V, ∂V);
Matrix ∂∂H = V.transpose() * ∂∂V + ∂V.transpose() * ∂V;
return {H, ∂H, ∂∂H};
@@ -104,10 +104,10 @@ public:
}
std::tuple<Real, Vector> getHamGrad(const Vector& x) const {
- auto [V, dV, ddV] = V_∂V_∂∂V(x);
+ auto [V, ∂V, ∂∂V] = V_∂V_∂∂V(x);
Real H = HFromV(V);
- Vector ∂H = dHFromVdV(V, dV);
+ Vector ∂H = ∂HFromV∂V(V, ∂V);
Vector ∇H = makeTangent(∂H, x);
return {H, ∇H};
@@ -128,12 +128,12 @@ public:
return eigenS.eigenvalues().real();
}
- Real maximumEigenvalue(const Vector& x) const {
+ Real maxEigenvalue(const Vector& x) const {
return spectrum(x).maxCoeff();
}
};
-Vector gradientDescent(const QuadraticModel& M, const Vector& x0, Real ε = 1e-13) {
+Vector gradientAscent(const QuadraticModel& M, const Vector& x0, Real ε = 1e-13) {
Vector x = x0;
Real α = 1;
Real m, H;
@@ -180,9 +180,9 @@ Vector subagAlgorithm(const QuadraticModel& M, Rng& r, unsigned k) {
int main(int argc, char* argv[]) {
unsigned N = 10;
Real α = 1;
- Real σ = 1;
- Real A = 1;
- Real J = 1;
+ Real σ² = 1;
+ Real μA = 1;
+ Real μJ = 1;
unsigned samples = 10;
int opt;
@@ -196,13 +196,13 @@ int main(int argc, char* argv[]) {
α = atof(optarg);
break;
case 's':
- σ = atof(optarg);
+ σ² = atof(optarg);
break;
case 'A':
- A = atof(optarg);
+ μA = atof(optarg);
break;
case 'J':
- J = atof(optarg);
+ μJ = atof(optarg);
break;
case 'n':
samples = atoi(optarg);
@@ -220,14 +220,14 @@ int main(int argc, char* argv[]) {
x(0) = sqrt(N);
for (unsigned sample = 0; sample < samples; sample++) {
- QuadraticModel leastSquares(N, M, r, σ, A, J);
- Vector xGD = gradientDescent(leastSquares, x);
- std::cout << leastSquares.getHamiltonian(xGD) / N << " " << leastSquares.maximumEigenvalue(xGD) << " ";
-
- leastSquares = QuadraticModel(N, M, r, σ, A, J);
- Vector xMP = subagAlgorithm(leastSquares, r, N);
- xMP = gradientDescent(leastSquares, xMP);
- std::cout << leastSquares.getHamiltonian(xMP) / N << " " << leastSquares.maximumEigenvalue(xMP) << std::endl;
+ QuadraticModel ls(N, M, r, σ², μA, μJ);
+ Vector xGD = gradientAscent(ls, x);
+ std::cout << ls.getHamiltonian(xGD) / N << " " << ls.maxEigenvalue(xGD) << " ";
+
+ ls = QuadraticModel(N, M, r, σ², μA, μJ);
+ Vector xMP = subagAlgorithm(ls, r, N);
+ xMP = gradientAscent(ls, xMP);
+ std::cout << ls.getHamiltonian(xMP) / N << " " << ls.maxEigenvalue(xMP) << std::endl;
}
return 0;
--
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