Got rid of min/max_range to exclusively use Cauchy's Bound. Updated quadratic solve to handle complex roots.

src/polysolve/__init__.py

@@ -1,4 +1,5 @@
 import math
+import cmath
 import numpy as np
 import numba
 from dataclasses import dataclass
@@ -104,8 +105,6 @@ class GA_Options:
                               (e.g., 7) is more precise but may return
                               multiple near-identical roots. Default: 5
     """
-    min_range: float = 0.0
-    max_range: float = 0.0
     num_of_generations: int = 10
     data_size: int = 100000
     mutation_strength: float = 0.01
@@ -359,20 +358,9 @@ class Function:
         mutation_size = int(data_size * mutation_ratio)
         random_size = data_size - elite_size - crossover_size - mutation_size
 
-        # Check if the user is using the default, non-expert range
-        user_range_is_default = (options.min_range == 0.0 and options.max_range == 0.0)
-
-        if user_range_is_default:
-            # User hasn't specified a custom range.
-            # We are the expert; use the smart, guaranteed bound.
-            bound = _get_cauchy_bound(self.coefficients)
-            min_r = -bound
-            max_r = bound
-        else:
-            # User has provided a custom range.
-            # Trust the expert; use their range.
-            min_r = options.min_range
-            max_r = options.max_range
+        bound = _get_cauchy_bound(self.coefficients)
+        min_r = -bound
+        max_r = bound
 
         # Create initial random solutions
         solutions = np.random.uniform(min_r, max_r, data_size)
@@ -488,20 +476,9 @@ class Function:
         fitness_gpu = cupy.RawKernel(_FITNESS_KERNEL_FLOAT, 'fitness_kernel')
         d_coefficients = cupy.array(self.coefficients, dtype=cupy.float64)
         
-        # Check if the user is using the default, non-expert range
-        user_range_is_default = (options.min_range == 0.0 and options.max_range == 0.0)
-
-        if user_range_is_default:
-            # User hasn't specified a custom range.
-            # We are the expert; use the smart, guaranteed bound.
-            bound = _get_cauchy_bound(self.coefficients)
-            min_r = -bound
-            max_r = bound
-        else:
-            # User has provided a custom range.
-            # Trust the expert; use their range.
-            min_r = options.min_range
-            max_r = options.max_range
+        bound = _get_cauchy_bound(self.coefficients)
+        min_r = -bound
+        max_r = bound
 
         # Create initial random solutions on the GPU
         d_solutions = cupy.random.uniform(
@@ -781,10 +758,7 @@ class Function:
 
         discriminant = (b**2) - (4*a*c)
 
-        if discriminant < 0:
-            return None  # No real roots
-
-        sqrt_discriminant = math.sqrt(discriminant)
+        sqrt_discriminant = cmath.sqrt(discriminant)
         
         # 1. Calculate the first root.
         # We use math.copysign(val, sign) to get the sign of b.