diff --git a/pathfinder/gradient_descent.py b/pathfinder/gradient_descent.py
index 4718a25..3aabf87 100644
--- a/pathfinder/gradient_descent.py
+++ b/pathfinder/gradient_descent.py
@@ -1,25 +1,29 @@
 import numpy
 
 
-def find_sols(cost_array, jacobian, step_size=0.001, max_iterations=5, initial=(0, 0), desired_cost=1e-6, step_size_tries=10):
+def find_sols(cost_array, jacobian, step_size=0.001, max_iterations=5, initial=(0, 0), desired_cost=1e-6, step_size_tries=30):
 	desired_cost_squared = desired_cost**2
 	current = numpy.array(initial)
 	iterations = 0
 	curr_cost = numpy.array(cost_array(current))
+
+	def total_cost(x):
+		cost = numpy.array(cost_array(x))
+		return cost.dot(cost)
+
 	while iterations < max_iterations:
 		curr_cost = numpy.array(cost_array(current))
 		if curr_cost.dot(curr_cost) < desired_cost_squared:
 			return ("Finished early", iterations, current)
 		gradient = .5 * numpy.matmul(numpy.transpose(jacobian(current)), curr_cost)
-		next = current - step_size * gradient
-		next_cost = numpy.array(cost_array(next))
+
 		tries = step_size_tries
 		current_step = step_size
-		while tries > 0 and next_cost.dot(next_cost) > curr_cost.dot(curr_cost):
-			current_step = current_step / 10
-			next = current - current_step / 10 * gradient
-			next_cost = numpy.array(cost_array(next))
+		while total_cost(current - current_step * gradient) > (total_cost(current) - 0.5 * current_step * gradient.dot(gradient)):
+			current_step = current_step * .8
 			tries -= 1
-		current = next
+			if tries == 0:
+				return ("hit minimum step size", iterations, current)
+		current = current - current_step * gradient
 		iterations += 1
 	return ("Ran out of iterations", iterations, current)
diff --git a/tests/test_gradient_descent.py b/tests/test_gradient_descent.py
index dc7406e..849b8a7 100644
--- a/tests/test_gradient_descent.py
+++ b/tests/test_gradient_descent.py
@@ -36,9 +36,11 @@ def test_find_sols():
 		x, y = pt
 		return numpy.array([[-2 * x, -2 * y], [-2 * (x - 8), -2 * (y + 8)]])
 
-	print(costs([3, 4]))
-	result = pathfinder.gradient_descent.find_sols(costs, jac, step_size=0.01, max_iterations=5000, initial=(2, 10), desired_cost=1e-6)
-	print(result)
+	message, iterations, result = pathfinder.gradient_descent.find_sols(costs, jac, step_size=0.01, max_iterations=5000, initial=(2, 10), desired_cost=1e-6)
+	numpy.testing.assert_almost_equal(
+		result, (3, 4),
+		decimal=7, err_msg='the result was off', verbose=True
+	)
 
 
 def dipole_cost(vn, xn_raw):
@@ -59,11 +61,11 @@ def test_actual_dipole_finding():
 		p = pt[0:3]
 		return (p.dot(p) - 35)
 
-	v1 = -0.0554777
-	v2 = -0.0601857
-	v3 = -0.0636403
-	v4 = -0.0648838
-	v5 = -0.0629715
+	v1 = -0.05547767706400186526225414
+	v2 = -0.06018573388098888319642888
+	v3 = -0.06364032191901859480476888
+	v4 = -0.06488383879243851188402150
+	v5 = -0.06297148063759813929659130
 
 	# the 0 here is a red herring for index purposes later
 	vns = [0, v1, v2, v3, v4, v5]
@@ -106,5 +108,8 @@ def test_actual_dipole_finding():
 			jac_row(5)(pt),
 		])
 
-	result = pathfinder.gradient_descent.find_sols(costs, jac, step_size=0.01, max_iterations=10000, initial=(1, 2, 3, 4, 5, 6), desired_cost=1e-6, step_size_tries=25)
-	print(result)
+	_, _, result = pathfinder.gradient_descent.find_sols(costs, jac, step_size=1, max_iterations=10000, initial=(1, 2, 3, 4, 5, 6), desired_cost=1e-6, step_size_tries=30)
+	numpy.testing.assert_allclose(
+		result, (1, 3, 5, 5, 6, 7),
+		rtol=5e-2, err_msg='the result was off', verbose=True
+	)