add timings plot

README.md

@@ -109,6 +109,12 @@ This example is taken from Example 5.3 in [1]. We minimize the q-norm $||x||_q$
 
 [Link to example script](examples/example_residual.py)
 
+When $q\geq 1$ the problem is convex and rather easy to solve. For $q<1$, we observed that the number of sample points need to be increased a lot in order to make the subproblems solvable in reasonable time.
+
+This problem has dimension 256, with one scalar constraint. To give a feeling for runtime, below is the runtime per iteration (for $q=1$), split into the main parts: sample points and compute gradients (`sample_and_grad`), solve the quadratic subproblem (`subproblem`), do the update step of iterate and approximate Hessian (`step`), and all other routines. 
+
+![Timings for SQP-GS with dim 256](data/img/timings_residual.png "Timings for SQP-GS with dim 256")
+
 ### Pretrained neural network constraint
 
 This toy example illustrates how to use a pretrained neural network as constraint function in `ncOPT`. We train a simple model to learn the mapping $(x_1,x_2) \mapsto \max(\sqrt{2}x_1, 2x_2) -1 $. Then, we load the model checkpoint to use it as constraint.

examples/example_residual.py

@@ -72,6 +72,7 @@
 # %% Plotting
 
 fig, ax = problem.plot_timings()
+fig.savefig("../data/img/timings_residual.png")
 fig, ax = problem.plot_metrics()
 
 

src/ncopt/plot_utils.py

@@ -23,7 +23,7 @@ def plot_timings(timings, ax=None):
             if None not in val:
                 summed += np.array(val)
 
-    ax.plot(summed, lw=1, ls="--", color="grey", label="summed")
+    # ax.plot(summed, lw=1, ls="--", color="grey", label="summed")
 
     ax.set_xlabel("Iteration")
     ax.set_ylabel("Runtime [sec]")