14 experiments with alternative implementations (#15)

* experiments

* experiments

Makefile

@@ -4,7 +4,7 @@
 
 venv:
 	curl -LsSf https://astral.sh/uv/install.sh | sh
-	uv venv
+	uv venv --python '3.12'
 
 install: venv ## Install dependencies and setup environment
 	uv pip install --upgrade pip

experiments/__init__.py

@@ -0,0 +1,13 @@
+#    Copyright 2025 Stanford University Convex Optimization Group
+#
+#    Licensed under the Apache License, Version 2.0 (the "License");
+#    you may not use this file except in compliance with the License.
+#    You may obtain a copy of the License at
+#
+#        http://www.apache.org/licenses/LICENSE-2.0
+#
+#    Unless required by applicable law or agreed to in writing, software
+#    distributed under the License is distributed on an "AS IS" BASIS,
+#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#    See the License for the specific language governing permissions and
+#    limitations under the License.

experiments/alter1.py

@@ -0,0 +1,23 @@
+import cvxpy as cp
+
+from cvx.ball.utils.circle import Center, Circle
+
+
+def min_circle_cvx(points, **kwargs):
+    # Use con_1 if no constraint construction is defined
+    # cvxpy variable for the radius
+    r = cp.Variable(name="Radius")
+    # cvxpy variable for the midpoint
+    x = cp.Variable(points.shape[1], name="Midpoint")
+    objective = cp.Minimize(r)
+    constraints = [cp.SOC(r, point - x) for point in points]
+    # * #np.ones(points.shape[0]),
+    #        points - cp.outer(np.ones(points.shape[0]), x),
+    #        axis=1,
+    #    )
+    # ]
+
+    problem = cp.Problem(objective=objective, constraints=constraints)
+    problem.solve(**kwargs)
+
+    return Circle(radius=float(r.value), center=Center(x.value))

experiments/alter2.py

@@ -0,0 +1,23 @@
+import cvxpy as cp
+
+from cvx.ball.utils.circle import Center, Circle
+
+
+def min_circle_cvx(points, **kwargs):
+    # Use con_1 if no constraint construction is defined
+    # cvxpy variable for the radius
+    r = cp.Variable(name="Radius")
+    # cvxpy variable for the midpoint
+    x = cp.Variable(points.shape[1], name="Midpoint")
+    objective = cp.Minimize(r)
+    constraints = [cp.norm2(x - point) <= r for point in points]
+    # * #np.ones(points.shape[0]),
+    #        points - cp.outer(np.ones(points.shape[0]), x),
+    #        axis=1,
+    #    )
+    # ]
+
+    problem = cp.Problem(objective=objective, constraints=constraints)
+    problem.solve(**kwargs)
+
+    return Circle(radius=float(r.value), center=Center(x.value))

experiments/asymptotic.py

@@ -0,0 +1,82 @@
+import time
+from typing import List, Tuple
+
+import numpy as np
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+from cvx.ball.solver import min_circle_cvx
+
+
+def cvx(n: int) -> float:
+    points = np.random.rand(n, 5)
+    return min_circle_cvx(points, solver="CLARABEL")
+
+
+def measure_execution_time(func, n: int, num_trials: int = 3) -> float:
+    """Run multiple trials and return average execution time"""
+    times = []
+    for _ in range(num_trials):
+        start = time.time()
+        func(n)
+        times.append(time.time() - start)
+    return np.mean(times)
+
+
+def run_analysis() -> Tuple[List[int], List[float]]:
+    # Test for different values of n (powers of 2)
+    sequence = np.array([2**n for n in range(4, 20)])
+    execution_times = []
+
+    for n in sequence:
+        avg_time = measure_execution_time(cvx, int(n))
+        execution_times.append(avg_time)
+        print(f"n={n}: {avg_time:.4f} seconds")
+
+    return sequence, execution_times
+
+
+def plot_results(sizes: List[int], times: List[float]) -> None:
+    # Create figure
+    fig = make_subplots(specs=[[{"secondary_y": True}]])
+
+    # Add actual execution times
+    fig.add_trace(
+        go.Scatter(
+            x=sizes, y=times, name="Actual Time", mode="lines+markers", line=dict(color="blue"), marker=dict(size=8)
+        )
+    )
+
+    # Add theoretical O(n) complexity line
+    normalized_n = np.array(sizes) / sizes[0]
+    fig.add_trace(
+        go.Scatter(x=sizes, y=normalized_n * times[0], name="O(n)", line=dict(color="red", dash="dash"), mode="lines")
+    )
+
+    # Update layout with log scales
+    fig.update_layout(
+        title="Algorithm Performance Analysis",
+        xaxis=dict(
+            title="Input Size (n)",
+            type="log",
+            dtick="D1",  # Show ticks for each power of 10
+        ),
+        yaxis=dict(title="Execution Time (seconds)", type="log", dtick="D1"),
+        hovermode="x unified",
+        showlegend=True,
+        legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.99),
+        plot_bgcolor="white",
+    )
+
+    # Add grid lines
+    fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor="LightGray")
+    fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor="LightGray")
+
+    # Show the plot
+    fig.show()
+
+
+if __name__ == "__main__":
+    # Run the analysis
+    sizes, times = run_analysis()
+    plot_results(sizes, times)

experiments/speed.py

@@ -0,0 +1,32 @@
+import statistics
+import timeit as tt
+
+import numpy as np
+
+from cvx.ball.solver import min_circle_cvx
+from experiments.alter1 import min_circle_cvx as alter1
+from experiments.alter2 import min_circle_cvx as alter2
+
+if __name__ == "__main__":
+    points = np.random.randn(10000, 5)
+
+    def cvx():
+        min_circle_cvx(points, solver="CLARABEL")
+
+    def alter_a():
+        alter1(points, solver="CLARABEL")
+
+    def alter_b():
+        alter2(points, solver="CLARABEL")
+
+    times_clarabel = tt.repeat(cvx, number=1, repeat=5)
+    print(times_clarabel)
+    print(statistics.mean(times_clarabel))
+
+    times_alter1 = tt.repeat(alter_a, number=1, repeat=5)
+    print(times_alter1)
+    print(statistics.mean(times_alter1))
+
+    times_alter2 = tt.repeat(alter_b, number=1, repeat=5)
+    print(times_alter2)
+    print(statistics.mean(times_alter2))

pyproject.toml

@@ -39,7 +39,7 @@ requires = ["hatchling"]
 build-backend = "hatchling.build"
 
 [tool.hatch.build.targets.wheel]
-packages = ["src/cvxball"]
+packages = ["src/cvx"]
 
 [tool.deptry.per_rule_ignores]
 DEP002 = ["clarabel"]