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doc/3-ProxQP_solve.md

@@ -65,7 +65,7 @@ $$\begin{equation}\label{eq:approx_qp_sol_relative_criterion}
 
 \section OverviewAsingleSolveFunction A single solve function for dense and sparse backends
 
-If if you don't want to pass through [ProxQP API](2-ProxQP_api.md), it is also possible to use one single solve function. We will show how to do so with examples. 
+If if you don't want to pass through [ProxQP API](2-ProxQP_api.md), it is also possible to use one single solve function. We will show how to do so with examples.
 
 You just need to call a "solve" function with in entry the model of the convex QP you want to solve. We show you below examples in C++ and python for ProxQP sparse and dense backends. Note that the sparse and dense solvers take respectivaly entries in sparse and dense formats.
 

examples/cpp/solve_without_api.cpp

@@ -32,7 +32,8 @@ main()
   std::cout << "optimal z from sparse solver: " << results_sparse_solver.z
             << std::endl;
   // Solve the problem using the dense backend
-  Results<T> results_dense_solver = dense::solve<T>(dense::Mat<T>(H), g,dense::Mat<T>(A), b,dense::Mat<T>(C), u, l);
+  Results<T> results_dense_solver = dense::solve<T>(
+    dense::Mat<T>(H), g, dense::Mat<T>(A), b, dense::Mat<T>(C), u, l);
   // print an optimal solution x,y and z
   std::cout << "optimal x from dense solver: " << results_dense_solver.x
             << std::endl;

examples/python/solve_without_api.py

@@ -40,7 +40,9 @@ def generate_mixed_qp(n, seed=1):
 
 # solve the problem using the dense backend
 
-results2 = proxsuite.proxqp.dense.solve(H.toarray(), g, A.toarray(), b, C.toarray(), u, l)
+results2 = proxsuite.proxqp.dense.solve(
+    H.toarray(), g, A.toarray(), b, C.toarray(), u, l
+)
 # Note finally, that the matrices are in sparse format, when using the dense backend you
 # should convert them in dense format
 

test/src/dense_qp_wrapper.py

@@ -3276,7 +3276,9 @@ def test_case_update_A_for_different_initial_guess(self):
             True,
         )
         qp.solve()
-        A = spa.random(n_eq, n, density=0.15, data_rvs=np.random.randn, format="csc").toarray()
+        A = spa.random(
+            n_eq, n, density=0.15, data_rvs=np.random.randn, format="csc"
+        ).toarray()
         dua_res = normInf(
             H @ qp.results.x
             + g