Merge branch 'add-sumfunctionalconstrainedlinearization' into 'master'

Add SumFunctionalConstrainedLinearization class

See merge request !20

dune/tnnmg/functionals/CMakeLists.txt

@@ -12,5 +12,6 @@ install(FILES
     quadraticfunctionalconstrainedlinearization.hh
     stronglyconvexfunctional.hh
     sumfunctional.hh
+    sumfunctionalconstrainedlinearization.hh
     zerofunctional.hh
     DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/dune/tnnmg/functionals)

dune/tnnmg/functionals/sumfunctionalconstrainedlinearization.hh

+#ifndef DUNE_TNNMG_SUMFUNCTIONALCONSTRAINEDLINEARIZATION_HH
+#define DUNE_TNNMG_SUMFUNCTIONALCONSTRAINEDLINEARIZATION_HH
+
+#include <tuple>
+
+#include <dune/common/hybridutilities.hh>
+
+#include <dune/solvers/common/resize.hh>
+
+namespace Dune::TNNMG
+{
+/**
+ * \brief A constrained linearization for a sum of functionals
+ *
+ * \tparam F The SumFunctional to be linearized
+ * \tparam BV Bit vector types for the degrees of freedom to be ignored
+ * \tparam Addends A list of ConstrainedLinearization implementations,
+ *     corresponding to the sum functional F
+ */
+template<class F, class BV, class... Addends>
+class SumFunctionalConstrainedLinearization
+{
+public:
+  using Matrix = typename std::tuple_element_t<0,typename F::Functions>::Matrix;
+  using Vector = typename F::Vector;
+  using BitVector = BV;
+  using ConstrainedVector = Vector;
+  using ConstrainedMatrix = Matrix;
+  using ConstrainedBitVector = BitVector;
+
+  /** \brief Constructor
+   */
+  SumFunctionalConstrainedLinearization(const F& f, const BitVector& ignore)
+  : addends_(std::apply([&](auto&&... fi) {
+      return std::make_tuple(Addends(fi, ignore)...);
+    }, f.functions())),
+    ignore_(ignore)
+  {}
+
+  /** \brief Pre-compute derivative information at the configuration x
+   */
+  void bind(const Vector& x)
+  {
+    Hybrid::forEach(addends_, [&](auto&& addend) {
+      addend.bind(x);
+    });
+
+    // Compute first derivatives of the functionals
+    Solvers::resizeInitializeZero(negativeGradient_, x);
+
+    Hybrid::forEach(addends_, [&](auto&& addend) {
+      negativeGradient_ += addend.negativeGradient();
+    });
+
+    // Compute second derivatives of the functionals
+    hessian_ = std::get<0>(addends_).hessian();
+
+    Hybrid::forEach(Hybrid::integralRange(std::integral_constant<int, 1>(),Hybrid::size(addends_)), [&](auto i) {
+      hessian_ += std::get<i>(addends_).hessian();
+    });
+
+    ////////////////////////////////////////////////////
+    // Determine which dofs to truncate
+    ////////////////////////////////////////////////////
+
+    // Truncate all degrees of freedom explicitly requested in the constructor
+    truncationFlags_ = ignore_;
+
+    // Also truncate a degree of freedom if one of the addends wants it truncated
+    for (std::size_t i=0; i<truncationFlags_.size(); i++)
+      Hybrid::forEach(addends_, [&](auto&& addend) {
+        truncationFlags_[i] |= addend.truncated()[i];
+      });
+
+    /////////////////////////////////////////////////////
+    // Do the actual truncation
+    /////////////////////////////////////////////////////
+
+    for (std::size_t i=0; i<x.size(); ++i)
+    {
+      // Truncate the gradient
+      for (std::size_t j=0; j<x[i].size(); ++j)
+        if (truncationFlags_[i][j])
+          negativeGradient_[i][j] = 0;
+
+      // Truncate the Hesse matrix
+      for (auto&& [entry, l] : sparseRange(hessian_[i]))
+        for (std::size_t j=0; j<x[i].size(); ++j)
+          for (std::size_t k=0; k<x[i].size(); ++k)
+            if (truncationFlags_[i][j] || truncationFlags_[l][k])
+            {
+              entry[j][k] = 0;
+#ifndef GAUSSSEIDEL
+              if (j==k && i == l)
+                entry[j][k] = 1;
+#endif
+            }
+
+    }
+  }
+
+  /** \brief Fill the truncated degrees of freedom in a vector with zeros
+   *
+   * \param cv Input vector
+   * \param[out] v Output vector: will be a copy of cv, with the truncated
+   *   degrees of freedom overwritten by zero
+   */
+  void extendCorrection(const ConstrainedVector& cv, Vector& v) const
+  {
+    for (std::size_t i=0; i<v.size(); ++i)
+      for (std::size_t j=0; j<v[i].size(); ++j)
+        v[i][j] = (truncationFlags_[i][j]) ? 0 : cv[i][j];
+  }
+
+  /** \brief Access to which degrees of freedom have been truncated */
+  const BitVector& truncated() const
+  {
+    return truncationFlags_;
+  }
+
+  /** \brief The negative gradient of the sum functional
+   *
+   * Only call this after a call to bind()!
+   */
+  const auto& negativeGradient() const
+  {
+    return negativeGradient_;
+  }
+
+  /** \brief The Hesse matrix of the sum functional
+   *
+   * Only call this after a call to bind()!
+   */
+  const auto& hessian() const
+  {
+    return hessian_;
+  }
+
+private:
+
+  // The ConstrainedLinearization objects that are being summed
+  std::tuple<Addends...> addends_;
+
+  // Mark degrees of freedom that should be truncated
+  const BitVector& ignore_;
+
+  Vector negativeGradient_;
+  Matrix hessian_;
+  BitVector truncationFlags_;
+};
+
+}   // namespace Dune::TNNMG
+
+#endif    //  DUNE_TNNMG_SUMFUNCTIONALCONSTRAINEDLINEARIZATION_HH