Fix a bug in EmbeddedExplicitRungeKutta default constructor

- Fix a bug where some variables were not initialized.
- Fix documentation in RungeKutta
- Remove unused variable in ImplicitRungeKutta

diff --git a/doc/news/changes/minor/20210830Turcksin b/doc/news/changes/minor/20210830Turcksin
new file mode 100644 (file)
index 0000000..0980c83
--- /dev/null
+++ b/doc/news/changes/minor/20210830Turcksin
@@ -0,0 +1,6 @@
+Fixed: There was a bug in the default constructor of
+TimeStepping::EmbeddedExplicitRungeKutta(). Some parameters were left
+uninitialized which triggered a segmentation fault when calling
+`evolve_one_time_step()`.
+<br>
+(Praveen Chandrashekar, Bruno Turcksin, 2021/08/30)

diff --git a/include/deal.II/base/time_stepping.h b/include/deal.II/base/time_stepping.h
index 0699e1a543bf127d557a846dacf3fc093849ad45..d3a5d9fd23e0b7a30f61efd291f3810a683fe2ac 100644 (file)
--- a/include/deal.II/base/time_stepping.h
+++ b/include/deal.II/base/time_stepping.h
@@ -261,7 +261,7 @@ namespace TimeStepping
      * the output is value of f at this point. @p id_minus_tau_J_inverse is a
      * function that computes $ inv(I-\tau J)$ where $ I $ is the identity
      * matrix, $ \tau $ is given, and $ J $ is the Jacobian $ \frac{\partial
-     * J}{\partial y} $. The input parameters are the time, $ \tau $, and a
+     * f}{\partial y} $. The input parameters are the time, $ \tau $, and a
      * vector. The output is the value of function at this point.
      * evolve_one_time_step returns the time at the end of the time step.
      */
@@ -333,10 +333,12 @@ namespace TimeStepping
      * parameters are the time t and the vector y and the output is value of f
      * at this point. @p id_minus_tau_J_inverse is a function that computes $
      * inv(I-\tau J)$ where $ I $ is the identity matrix, $ \tau $ is given,
-     * and $ J $ is the Jacobian $ \frac{\partial J}{\partial y} $. The input
+     * and $ J $ is the Jacobian $ \frac{\partial f}{\partial y} $. The input
      * parameter are the time, $ \tau $, and a vector. The output is the value
      * of function at this point. evolve_one_time_step returns the time at the
      * end of the time step.
+     *
+     * @note @p id_minus_tau_J_inverse is ignored since the method is explicit.
      */
     double
     evolve_one_time_step(
@@ -435,7 +437,7 @@ namespace TimeStepping
      * parameters are the time t and the vector y and the output is value of f
      * at this point. @p id_minus_tau_J_inverse is a function that computes $
      * inv(I-\tau J)$ where $ I $ is the identity matrix, $ \tau $ is given,
-     * and $ J $ is the Jacobian $ \frac{\partial J}{\partial y} $. The input
+     * and $ J $ is the Jacobian $ \frac{\partial f}{\partial y} $. The input
      * parameters are the time, $ \tau $, and a vector. The output is the value
      * of function at this point. evolve_one_time_step returns the time at the
      * end of the time step.
@@ -558,7 +560,7 @@ namespace TimeStepping
      * parameters are the time t and the vector y and the output is value of f
      * at this point. @p id_minus_tau_J_inverse is a function that computes $
      * (I-\tau J)^{-1}$ where $ I $ is the identity matrix, $ \tau $ is given,
-     * and $ J $ is the Jacobian $ \frac{\partial J}{\partial y} $. The input
+     * and $ J $ is the Jacobian $ \frac{\partial f}{\partial y} $. The input
      * parameters this function receives are the time, $ \tau $, and a vector.
      * The output is the value of function at this point. evolve_one_time_step
      * returns the time at the end of the time step.
@@ -642,13 +644,6 @@ namespace TimeStepping
       VectorType &      tendency,
       VectorType &      residual) const;
 
-    /**
-     * When using SDIRK, there is no need to compute the linear combination of
-     * the stages. Thus, when this flag is true, the linear combination is
-     * skipped.
-     */
-    bool skip_linear_combi;
-
     /**
      * Maximum number of iterations of the Newton solver.
      */
@@ -722,10 +717,12 @@ namespace TimeStepping
      * parameters are the time t and the vector y and the output is value of f
      * at this point. @p id_minus_tau_J_inverse is a function that computes $
      * inv(I-\tau J)$ where $ I $ is the identity matrix, $ \tau $ is given,
-     * and $ J $ is the Jacobian $ \frac{\partial J}{\partial y} $. The input
+     * and $ J $ is the Jacobian $ \frac{\partial f}{\partial y} $. The input
      * parameters are the time, $ \tau $, and a vector. The output is the
      * value of function at this point. evolve_one_time_step returns the time
      * at the end of the time step.
+     *
+     * @note @p id_minus_tau_J_inverse is ignored since the method is explicit.
      */
     double
     evolve_one_time_step(
@@ -833,7 +830,7 @@ namespace TimeStepping
      * If the flag is true, the last stage is the same as the first stage and
      * one evaluation of f can be saved.
      */
-    bool last_same_as_first;
+    bool last_same_as_first = false;
 
     /**
      * Butcher tableau coefficients.
@@ -849,7 +846,7 @@ namespace TimeStepping
      * If the last_same_as_first flag is set to true, the last stage is saved
      * and reused as the first stage of the next time step.
      */
-    VectorType *last_stage;
+    VectorType *last_stage = nullptr;
 
     /**
      * Status structure of the object.

diff --git a/include/deal.II/base/time_stepping.templates.h b/include/deal.II/base/time_stepping.templates.h
index 6bb8b682d8e9192c8e271e8d1130432a3a81ce56..cfc0ff43f1b16e5d68cf9e9cdd03a99bd37877ea 100644 (file)
--- a/include/deal.II/base/time_stepping.templates.h
+++ b/include/deal.II/base/time_stepping.templates.h
@@ -479,7 +479,6 @@ namespace TimeStepping
     const unsigned int       max_it,
     const double             tolerance)
     : RungeKutta<VectorType>()
-    , skip_linear_combi(false)
     , max_it(max_it)
     , tolerance(tolerance)
   {
@@ -572,13 +571,9 @@ namespace TimeStepping
     // Compute the different stages needed.
     compute_stages(f, id_minus_tau_J_inverse, t, delta_t, y, f_stages);
 
-    // If necessary, compute the linear combinations of the stages.
-    if (skip_linear_combi == false)
-      {
-        y = old_y;
-        for (unsigned int i = 0; i < this->n_stages; ++i)
-          y.sadd(1., delta_t * this->b[i], f_stages[i]);
-      }
+    y = old_y;
+    for (unsigned int i = 0; i < this->n_stages; ++i)
+      y.sadd(1., delta_t * this->b[i], f_stages[i]);
 
     return (t + delta_t);
   }