add specialization of check_sparse_solving() for SuperLU solver, in order to test adjoint and transpose solves

Eigen/src/SparseLU/SparseLU.h

@@ -18,6 +18,63 @@ template <typename _MatrixType, typename _OrderingType = COLAMDOrdering<typename
 template <typename MappedSparseMatrixType> struct SparseLUMatrixLReturnType;
 template <typename MatrixLType, typename MatrixUType> struct SparseLUMatrixUReturnType;
 
+template <bool Conjugate,class SparseLUType>
+class SparseLUTransposeView : public SparseSolverBase<SparseLUTransposeView<Conjugate,SparseLUType> >
+{
+protected:
+  typedef SparseSolverBase<SparseLUTransposeView<Conjugate,SparseLUType> > APIBase;
+  using APIBase::m_isInitialized;
+public:
+  typedef typename SparseLUType::Scalar Scalar;
+  typedef typename SparseLUType::StorageIndex StorageIndex;
+  typedef typename SparseLUType::MatrixType MatrixType;
+  typedef typename SparseLUType::OrderingType OrderingType;
+
+  enum {
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+
+  SparseLUTransposeView() : m_sparseLU(nullptr) {}
+  SparseLUTransposeView(const SparseLUTransposeView& view) {
+    this->m_sparseLU = view.m_sparseLU;
+  }
+  void setIsInitialized(const bool isInitialized) {this->m_isInitialized = isInitialized;}
+  void setSparseLU(SparseLUType* sparseLU) {m_sparseLU = sparseLU;}
+  using APIBase::_solve_impl;
+  template<typename Rhs, typename Dest>
+  bool _solve_impl(const MatrixBase<Rhs> &B, MatrixBase<Dest> &X_base) const
+  {
+    Dest& X(X_base.derived());
+    eigen_assert(m_sparseLU->info() == Success && "The matrix should be factorized first");
+    EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,
+                        THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+
+
+    // this ugly const_cast_derived() helps to detect aliasing when applying the permutations
+    for(Index j = 0; j < B.cols(); ++j){
+      X.col(j) = m_sparseLU->colsPermutation() * B.const_cast_derived().col(j);
+    }
+    //Forward substitution with transposed or adjoint of U
+    m_sparseLU->matrixU().template solveTransposedInPlace<Conjugate>(X);
+
+    //Backward substitution with transposed or adjoint of L
+    m_sparseLU->matrixL().template solveTransposedInPlace<Conjugate>(X);
+
+    // Permute back the solution
+    for (Index j = 0; j < B.cols(); ++j)
+      X.col(j) = m_sparseLU->rowsPermutation().transpose() * X.col(j);
+    return true;
+  }
+  inline Index rows() const { return m_sparseLU->rows(); }
+  inline Index cols() const { return m_sparseLU->cols(); }
+
+private:
+  SparseLUType *m_sparseLU;
+  SparseLUTransposeView& operator=(const SparseLUTransposeView&);
+};
+
+
 /** \ingroup SparseLU_Module
   * \class SparseLU
   * 
@@ -97,6 +154,7 @@ class SparseLU : public SparseSolverBase<SparseLU<_MatrixType,_OrderingType> >,
     };
     
   public:
+
     SparseLU():m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1)
     {
       initperfvalues(); 
@@ -129,6 +187,45 @@ class SparseLU : public SparseSolverBase<SparseLU<_MatrixType,_OrderingType> >,
       factorize(matrix);
     } 
 
+    /** \returns an expression of the transposed of the factored matrix.
+      *
+      * A typical usage is to solve for the transposed problem A^T x = b:
+      * \code
+      * solver.compute(A);
+      * x = solver.transpose().solve(b);
+      * \endcode
+      *
+      * \sa adjoint(), solve()
+      */
+    const SparseLUTransposeView<false,SparseLU<_MatrixType,_OrderingType> > transpose()
+    {
+      SparseLUTransposeView<false,  SparseLU<_MatrixType,_OrderingType> > transposeView;
+      transposeView.setSparseLU(this);
+      transposeView.setIsInitialized(this->m_isInitialized);
+      return transposeView;
+    }
+
+
+    /** \returns an expression of the adjoint of the factored matrix
+      *
+      * A typical usage is to solve for the adjoint problem A' x = b:
+      * \code
+      * solver.compute(A);
+      * x = solver.adjoint().solve(b);
+      * \endcode
+      *
+      * For real scalar types, this function is equivalent to transpose().
+      *
+      * \sa transpose(), solve()
+      */
+    const SparseLUTransposeView<true, SparseLU<_MatrixType,_OrderingType> > adjoint()
+    {
+      SparseLUTransposeView<true,  SparseLU<_MatrixType,_OrderingType> > adjointView;
+      adjointView.setSparseLU(this);
+      adjointView.setIsInitialized(this->m_isInitialized);
+      return adjointView;
+    }
+    
     inline Index rows() const { return m_mat.rows(); }
     inline Index cols() const { return m_mat.cols(); }
     /** Indicate that the pattern of the input matrix is symmetric */
@@ -394,7 +491,6 @@ class SparseLU : public SparseSolverBase<SparseLU<_MatrixType,_OrderingType> >,
   private:
     // Disable copy constructor 
     SparseLU (const SparseLU& );
-  
 }; // End class SparseLU
 
 
@@ -712,6 +808,12 @@ struct SparseLUMatrixLReturnType : internal::no_assignment_operator
   {
     m_mapL.solveInPlace(X);
   }
+  template<bool Conjugate, typename Dest>
+  void solveTransposedInPlace( MatrixBase<Dest> &X) const
+  {
+    m_mapL.template solveTransposedInPlace<Conjugate>(X);
+  }
+
   const MappedSupernodalType& m_mapL;
 };
 
@@ -766,6 +868,52 @@ struct SparseLUMatrixUReturnType : internal::no_assignment_operator
       }
     } // End For U-solve
   }
+
+  template<bool Conjugate, typename Dest>   void solveTransposedInPlace(MatrixBase<Dest> &X) const
+  {
+    using numext::conj;
+    Index nrhs = X.cols();
+    Index n    = X.rows();
+    // Forward solve with U
+    for (Index k = 0; k <=  m_mapL.nsuper(); k++)
+    {
+      Index fsupc = m_mapL.supToCol()[k];
+      Index lda = m_mapL.colIndexPtr()[fsupc+1] - m_mapL.colIndexPtr()[fsupc]; // leading dimension
+      Index nsupc = m_mapL.supToCol()[k+1] - fsupc;
+      Index luptr = m_mapL.colIndexPtr()[fsupc];
+
+      for (Index j = 0; j < nrhs; ++j)
+      {
+        for (Index jcol = fsupc; jcol < fsupc + nsupc; jcol++)
+        {
+          typename MatrixUType::InnerIterator it(m_mapU, jcol);
+          for ( ; it; ++it)
+          {
+            Index irow = it.index();
+            X(jcol, j) -= X(irow, j) * (Conjugate? conj(it.value()): it.value());
+          }
+        }
+      }
+      if (nsupc == 1)
+      {
+        for (Index j = 0; j < nrhs; j++)
+        {
+          X(fsupc, j) /= (Conjugate? conj(m_mapL.valuePtr()[luptr]) : m_mapL.valuePtr()[luptr]);
+        }
+      }
+      else
+      {
+        Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(m_mapL.valuePtr()[luptr]), nsupc, nsupc, OuterStride<>(lda) );
+        Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
+        if(Conjugate)
+          U = A.adjoint().template triangularView<Lower>().solve(U);
+        else
+          U = A.transpose().template triangularView<Lower>().solve(U);
+      }
+    }// End For U-solve
+  }
+
+
   const MatrixLType& m_mapL;
   const MatrixUType& m_mapU;
 };

Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h

@@ -156,6 +156,9 @@ class MappedSuperNodalMatrix
     class InnerIterator; 
     template<typename Dest>
     void solveInPlace( MatrixBase<Dest>&X) const;
+    template<bool Conjugate, typename Dest>
+    void solveTransposedInPlace( MatrixBase<Dest>&X) const;
+
     
       
       
@@ -294,6 +297,77 @@ void MappedSuperNodalMatrix<Scalar,Index_>::solveInPlace( MatrixBase<Dest>&X) co
     } 
 }
 
+template<typename Scalar, typename Index_>
+template<bool Conjugate, typename Dest>
+void MappedSuperNodalMatrix<Scalar,Index_>::solveTransposedInPlace( MatrixBase<Dest>&X) const
+{
+    using numext::conj;
+  Index n    = int(X.rows());
+  Index nrhs = Index(X.cols());
+  const Scalar * Lval = valuePtr();                 // Nonzero values
+  Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor> work(n, nrhs);     // working vector
+  work.setZero();
+  for (Index k = nsuper(); k >= 0; k--)
+  {
+    Index fsupc = supToCol()[k];                    // First column of the current supernode
+    Index istart = rowIndexPtr()[fsupc];            // Pointer index to the subscript of the current column
+    Index nsupr = rowIndexPtr()[fsupc+1] - istart;  // Number of rows in the current supernode
+    Index nsupc = supToCol()[k+1] - fsupc;          // Number of columns in the current supernode
+    Index nrow = nsupr - nsupc;                     // Number of rows in the non-diagonal part of the supernode
+    Index irow;                                     //Current index row
+
+    if (nsupc == 1 )
+    {
+      for (Index j = 0; j < nrhs; j++)
+      {
+        InnerIterator it(*this, fsupc);
+        ++it; // Skip the diagonal element
+        for (; it; ++it)
+        {
+          irow = it.row();
+          X(fsupc,j) -= X(irow, j) * (Conjugate?conj(it.value()):it.value());
+        }
+      }
+    }
+    else
+    {
+      // The supernode has more than one column
+      Index luptr = colIndexPtr()[fsupc];
+      Index lda = colIndexPtr()[fsupc+1] - luptr;
+
+      //Begin Gather
+      for (Index j = 0; j < nrhs; j++)
+      {
+        Index iptr = istart + nsupc;
+        for (Index i = 0; i < nrow; i++)
+        {
+          irow = rowIndex()[iptr];
+          work.topRows(nrow)(i,j)= X(irow,j); // Gather operation
+          iptr++;
+        }
+      }
+
+      // Matrix-vector product with transposed submatrix
+      Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(Lval[luptr+nsupc]), nrow, nsupc, OuterStride<>(lda) );
+      Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
+      if(Conjugate)
+        U = U - A.adjoint() * work.topRows(nrow);
+      else
+        U = U - A.transpose() * work.topRows(nrow);
+
+      // Triangular solve (of transposed diagonal block)
+      new (&A) Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > ( &(Lval[luptr]), nsupc, nsupc, OuterStride<>(lda) );
+      if(Conjugate)
+        U = A.adjoint().template triangularView<UnitUpper>().solve(U);
+      else
+        U = A.transpose().template triangularView<UnitUpper>().solve(U);
+
+    }
+
+  }
+}
+
+
 } // end namespace internal
 
 } // end namespace Eigen

test/sparse_solver.h

@@ -9,6 +9,7 @@
 
 #include "sparse.h"
 #include <Eigen/SparseCore>
+#include <Eigen/SparseLU>
 #include <sstream>
 
 template<typename Solver, typename Rhs, typename Guess,typename Result>
@@ -144,6 +145,136 @@ void check_sparse_solving(Solver& solver, const typename Solver::MatrixType& A,
   }
 }
 
+// specialization of generic check_sparse_solving for SuperLU in order to also test adjoint and transpose solves
+template<typename Scalar, typename Rhs, typename DenseMat, typename DenseRhs>
+void check_sparse_solving(Eigen::SparseLU<Eigen::SparseMatrix<Scalar> >& solver, const typename Eigen::SparseMatrix<Scalar>& A, const Rhs& b, const DenseMat& dA, const DenseRhs& db)
+{
+  typedef typename Eigen::SparseMatrix<Scalar> Mat;
+  typedef typename Mat::StorageIndex StorageIndex;
+  typedef typename Eigen::SparseLU<Eigen::SparseMatrix<Scalar> > Solver;
+
+  // reference solutions computed by dense QR solver
+  DenseRhs refX1 = dA.householderQr().solve(db); // solution of A x = db
+  DenseRhs refX2 = dA.transpose().householderQr().solve(db); // solution of A^T * x = db (use transposed matrix A^T)
+  DenseRhs refX3 = dA.adjoint().householderQr().solve(db);  // solution of A^* * x = db (use adjoint matrix A^*)
+
+
+  {
+    Rhs x1(A.cols(), b.cols());
+    Rhs x2(A.cols(), b.cols());
+    Rhs x3(A.cols(), b.cols());
+    Rhs oldb = b;
+
+    solver.compute(A);
+    if (solver.info() != Success)
+    {
+      std::cerr << "ERROR | sparse solver testing, factorization failed (" << typeid(Solver).name() << ")\n";
+      VERIFY(solver.info() == Success);
+    }
+    x1 = solver.solve(b);
+    if (solver.info() != Success)
+    {
+      std::cerr << "WARNING | sparse solver testing: solving failed (" << typeid(Solver).name() << ")\n";
+      return;
+    }
+    VERIFY(oldb.isApprox(b,0.0) && "sparse solver testing: the rhs should not be modified!");
+    VERIFY(x1.isApprox(refX1,test_precision<Scalar>()));
+
+    // test solve with transposed
+    x2 = solver.transpose().solve(b);
+    VERIFY(oldb.isApprox(b) && "sparse solver testing: the rhs should not be modified!");
+    VERIFY(x2.isApprox(refX2,test_precision<Scalar>()));
+
+
+    // test solve with adjoint
+    //solver.template _solve_impl_transposed<true>(b, x3);
+    x3 = solver.adjoint().solve(b);
+    VERIFY(oldb.isApprox(b,0.0) && "sparse solver testing: the rhs should not be modified!");
+    VERIFY(x3.isApprox(refX3,test_precision<Scalar>()));
+
+    x1.setZero();
+    solve_with_guess(solver, b, x1, x1);
+    VERIFY(solver.info() == Success && "solving failed when using analyzePattern/factorize API");
+    VERIFY(oldb.isApprox(b,0.0) && "sparse solver testing: the rhs should not be modified!");
+    VERIFY(x1.isApprox(refX1,test_precision<Scalar>()));
+
+    x1.setZero();
+    x2.setZero();
+    x3.setZero();
+    // test the analyze/factorize API
+    solver.analyzePattern(A);
+    solver.factorize(A);
+    VERIFY(solver.info() == Success && "factorization failed when using analyzePattern/factorize API");
+    x1 = solver.solve(b);
+    x2 = solver.transpose().solve(b);
+    x3 = solver.adjoint().solve(b);
+
+    VERIFY(solver.info() == Success && "solving failed when using analyzePattern/factorize API");
+    VERIFY(oldb.isApprox(b,0.0) && "sparse solver testing: the rhs should not be modified!");
+    VERIFY(x1.isApprox(refX1,test_precision<Scalar>()));
+    VERIFY(x2.isApprox(refX2,test_precision<Scalar>()));
+    VERIFY(x3.isApprox(refX3,test_precision<Scalar>()));
+
+    x1.setZero();
+    // test with Map
+    MappedSparseMatrix<Scalar,Mat::Options,StorageIndex> Am(A.rows(), A.cols(), A.nonZeros(), const_cast<StorageIndex*>(A.outerIndexPtr()), const_cast<StorageIndex*>(A.innerIndexPtr()), const_cast<Scalar*>(A.valuePtr()));
+    solver.compute(Am);
+    VERIFY(solver.info() == Success && "factorization failed when using Map");
+    DenseRhs dx(refX1);
+    dx.setZero();
+    Map<DenseRhs> xm(dx.data(), dx.rows(), dx.cols());
+    Map<const DenseRhs> bm(db.data(), db.rows(), db.cols());
+    xm = solver.solve(bm);
+    VERIFY(solver.info() == Success && "solving failed when using Map");
+    VERIFY(oldb.isApprox(bm,0.0) && "sparse solver testing: the rhs should not be modified!");
+    VERIFY(xm.isApprox(refX1,test_precision<Scalar>()));
+  }
+
+  // if not too large, do some extra check:
+  if(A.rows()<2000)
+  {
+    // test initialization ctor
+    {
+      Rhs x(b.rows(), b.cols());
+      Solver solver2(A);
+      VERIFY(solver2.info() == Success);
+      x = solver2.solve(b);
+      VERIFY(x.isApprox(refX1,test_precision<Scalar>()));
+    }
+
+    // test dense Block as the result and rhs:
+    {
+      DenseRhs x(refX1.rows(), refX1.cols());
+      DenseRhs oldb(db);
+      x.setZero();
+      x.block(0,0,x.rows(),x.cols()) = solver.solve(db.block(0,0,db.rows(),db.cols()));
+      VERIFY(oldb.isApprox(db,0.0) && "sparse solver testing: the rhs should not be modified!");
+      VERIFY(x.isApprox(refX1,test_precision<Scalar>()));
+    }
+
+    // test uncompressed inputs
+    {
+      Mat A2 = A;
+      A2.reserve((ArrayXf::Random(A.outerSize())+2).template cast<typename Mat::StorageIndex>().eval());
+      solver.compute(A2);
+      Rhs x = solver.solve(b);
+      VERIFY(x.isApprox(refX1,test_precision<Scalar>()));
+    }
+
+    // test expression as input
+    {
+      solver.compute(0.5*(A+A));
+      Rhs x = solver.solve(b);
+      VERIFY(x.isApprox(refX1,test_precision<Scalar>()));
+
+      Solver solver2(0.5*(A+A));
+      Rhs x2 = solver2.solve(b);
+      VERIFY(x2.isApprox(refX1,test_precision<Scalar>()));
+    }
+  }
+}
+
+
 template<typename Solver, typename Rhs>
 void check_sparse_solving_real_cases(Solver& solver, const typename Solver::MatrixType& A, const Rhs& b, const typename Solver::MatrixType& fullA, const Rhs& refX)
 {

unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h

@@ -52,7 +52,7 @@ public:
         Parameters()
             : factor(Scalar(100.))
             , maxfev(1000)
-            , xtol(std::sqrt(NumTraits<Scalar>::epsilon()))
+            , xtol(numext::sqrt(NumTraits<Scalar>::epsilon()))
             , nb_of_subdiagonals(-1)
             , nb_of_superdiagonals(-1)
             , epsfcn(Scalar(0.)) {}
@@ -70,7 +70,7 @@ public:
 
     HybridNonLinearSolverSpace::Status hybrj1(
             FVectorType  &x,
-            const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
+            const Scalar tol = numext::sqrt(NumTraits<Scalar>::epsilon())
             );
 
     HybridNonLinearSolverSpace::Status solveInit(FVectorType  &x);
@@ -79,7 +79,7 @@ public:
 
     HybridNonLinearSolverSpace::Status hybrd1(
             FVectorType  &x,
-            const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
+            const Scalar tol = numext::sqrt(NumTraits<Scalar>::epsilon())
             );
 
     HybridNonLinearSolverSpace::Status solveNumericalDiffInit(FVectorType  &x);