Fix missing negative on residual

src/gradient_descent/conjugate_gradient.rs

@@ -99,20 +99,18 @@ pub fn conjugate_gradient<T: XVar<E> + Clone, E: Debug + ConjGradPrime>(
         // Check for convergence
         f = fun.eval(&xs);
         if (f - f_iminus1).abs() < tolerance {
-            println!("{f} {f_iminus1}");
             break;
         } else {
             f_iminus1 = f;
         }
 
         // Update using polack-ribiere
-        let new_residual = fun.prime(&xs);
+        let new_residual = T::scale_prime(&fun.prime(&xs), -1.0);
-        let beta = new_residual
+        let beta = (new_residual.mul(&new_residual.sub(&prev_residual)))
-            .mul(&new_residual.sub(&prev_residual))
+            .div(&prev_residual.mul(&prev_residual));
-            .div(&new_residual.mul(&new_residual));
         let beta = beta.max(0.0);
         direction = new_residual.add(&beta.mul(&direction));
-        prev_residual = new_residual;
+        prev_residual = new_residual.clone();
         i += 1;
     }
 
@@ -153,6 +151,7 @@ mod test {
                 res.best_fun_val, res.best_xs
             );
 
+            println!("Exitted with {:?}", res.exit_con);
             if let ExitCondition::MaxIter = res.exit_con {
                 panic!("Failed to converge to minima");
             }
@@ -186,11 +185,12 @@ mod test {
             gamma: 0.9,
             c: 0.01,
         };
-        let res = conjugate_gradient(&obj, &vec![3.1, 0.5], 10000, 1e-12, &line_search);
+        let res = conjugate_gradient(&obj, &vec![4.0, 1.00], 10000, 1e-12, &line_search);
         println!(
             "Best val is {:?} for xs {:?}",
             res.best_fun_val, res.best_xs
         );
+
         println!("Exit condition is: {:?}", res.exit_con);
         assert!(res.best_fun_val < 1e-7);
     }