From 4eadc4d7c85ad4e942a5280064c0072082d94026 Mon Sep 17 00:00:00 2001
From: Jaron Kent-Dobias <jaron@kent-dobias.com>
Date: Sun, 21 Apr 2024 11:21:31 +0200
Subject: Removed renaming of variable, and destruct first model to create
second.
---
least_squares.cpp | 13 +++++++------
1 file changed, 7 insertions(+), 6 deletions(-)
diff --git a/least_squares.cpp b/least_squares.cpp
index b294679..49100b4 100644
--- a/least_squares.cpp
+++ b/least_squares.cpp
@@ -158,7 +158,7 @@ Vector gradientDescent(const QuadraticModel& M, const Vector& x0, Real ε = 1e-7
return x;
}
-Vector findMinimum(const QuadraticModel& M, const Vector& x0, Real ε = 1e-5) {
+Vector levenbergMarquardt(const QuadraticModel& M, const Vector& x0, Real ε = 1e-5) {
Vector x = x0;
Real λ = 100;
@@ -182,7 +182,7 @@ Vector findMinimum(const QuadraticModel& M, const Vector& x0, Real ε = 1e-5) {
return x;
}
-Vector subagAlgorithm(const QuadraticModel& M, Rng& r, unsigned k, unsigned m) {
+Vector subagAlgorithm(const QuadraticModel& M, Rng& r, unsigned k) {
Vector σ = Vector::Zero(M.N);
unsigned axis = r.variate<unsigned, std::uniform_int_distribution>(0, M.N - 1);
σ(axis) = sqrt(M.N / k);
@@ -243,10 +243,11 @@ int main(int argc, char* argv[]) {
QuadraticModel leastSquares(N, M, r, σ, A, J);
x = gradientDescent(leastSquares, x);
std::cout << leastSquares.getHamiltonian(x) / N;
- QuadraticModel leastSquares2(N, M, r, σ, A, J);
- Vector σ = subagAlgorithm(leastSquares2, r, N, 15);
- σ = gradientDescent(leastSquares2, σ);
- std::cout << " " << leastSquares2.getHamiltonian(σ) / N << std::endl;
+
+ leastSquares = QuadraticModel(N, M, r, σ, A, J);
+ x = subagAlgorithm(leastSquares, r, N);
+ x = gradientDescent(leastSquares, x);
+ std::cout << " " << leastSquares.getHamiltonian(x) / N << std::endl;
}
return 0;
--
cgit v1.2.3-70-g09d2