Replace assert() by eigen_assert() (fixes bug #548).

20 files changed, 51 insertions, 51 deletions

diff --git a/unsupported/Eigen/src/IterativeSolvers/DGMRES.h b/unsupported/Eigen/src/IterativeSolvers/DGMRES.h
index 952aba15e..2ea0eccf1 100644
--- a/unsupported/Eigen/src/IterativeSolvers/DGMRES.h
+++ b/unsupported/Eigen/src/IterativeSolvers/DGMRES.h
@@ -38,7 +38,7 @@ struct traits<DGMRES<_MatrixType,_Preconditioner> >
 template <typename VectorType, typename IndexType>
 void sortWithPermutation (VectorType& vec, IndexType& perm, typename IndexType::Scalar& ncut)
 {
-  assert(vec.size() == perm.size());
+  eigen_assert(vec.size() == perm.size());
   typedef typename IndexType::Scalar Index; 
   typedef typename VectorType::Scalar Scalar; 
   Index n = vec.size();
@@ -525,4 +525,4 @@ struct solve_retval<DGMRES<_MatrixType, _Preconditioner>, Rhs>
 } // end namespace internal
 
 } // end namespace Eigen
-#endif 
\ No newline at end of file
+#endif 
diff --git a/unsupported/Eigen/src/IterativeSolvers/MINRES.h b/unsupported/Eigen/src/IterativeSolvers/MINRES.h
index 6bc1b8f5d..0e56342a8 100644
--- a/unsupported/Eigen/src/IterativeSolvers/MINRES.h
+++ b/unsupported/Eigen/src/IterativeSolvers/MINRES.h
@@ -52,7 +52,7 @@ namespace Eigen {
             VectorType w_new(precond.solve(v_new)); // initialize w_new
 //            RealScalar beta; // will be initialized inside loop
             RealScalar beta_new2(v_new.dot(w_new));
-            assert(beta_new2 >= 0 && "PRECONDITIONER IS NOT POSITIVE DEFINITE");
+            eigen_assert(beta_new2 >= 0 && "PRECONDITIONER IS NOT POSITIVE DEFINITE");
             RealScalar beta_new(sqrt(beta_new2));
             const RealScalar beta_one(beta_new);
             v_new /= beta_new;
@@ -91,7 +91,7 @@ namespace Eigen {
                 v_new -= alpha*v; // overwrite v_new
                 w_new = precond.solve(v_new); // overwrite w_new
                 beta_new2 = v_new.dot(w_new); // compute beta_new
-                assert(beta_new2 >= 0 && "PRECONDITIONER IS NOT POSITIVE DEFINITE");
+                eigen_assert(beta_new2 >= 0 && "PRECONDITIONER IS NOT POSITIVE DEFINITE");
                 beta_new = sqrt(beta_new2); // compute beta_new
                 v_new /= beta_new; // overwrite v_new for next iteration
                 w_new /= beta_new; // overwrite w_new for next iteration
diff --git a/unsupported/Eigen/src/IterativeSolvers/Scaling.h b/unsupported/Eigen/src/IterativeSolvers/Scaling.h
index ed4ee48c8..4fd439202 100644
--- a/unsupported/Eigen/src/IterativeSolvers/Scaling.h
+++ b/unsupported/Eigen/src/IterativeSolvers/Scaling.h
@@ -73,7 +73,7 @@ class IterScaling
     {
       int m = mat.rows(); 
       int n = mat.cols();
-      assert((m>0 && m == n) && "Please give a non - empty matrix");
+      eigen_assert((m>0 && m == n) && "Please give a non - empty matrix");
       m_left.resize(m); 
       m_right.resize(n);
       m_left.setOnes();
@@ -182,4 +182,4 @@ class IterScaling
     int m_maxits; // Maximum number of iterations allowed
 };
 }
-#endif
\ No newline at end of file
+#endif
diff --git a/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h b/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h
index 3210587e4..32d3ad518 100644
--- a/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h
+++ b/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h
@@ -33,7 +33,7 @@ void covar(
     const Index n = r.cols();
     const Scalar tolr = tol * abs(r(0,0));
     Matrix< Scalar, Dynamic, 1 > wa(n);
-    assert(ipvt.size()==n);
+    eigen_assert(ipvt.size()==n);
 
     /* form the inverse of r in the full upper triangle of r. */
     l = -1;
diff --git a/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h b/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h
index aa0c02668..51ade866c 100644
--- a/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h
+++ b/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h
@@ -26,7 +26,7 @@ LevenbergMarquardt<FunctorType>::minimizeOneStep(FVectorType  &x)
   RealScalar temp, temp1,temp2; 
   RealScalar ratio; 
   RealScalar pnorm, xnorm, fnorm1, actred, dirder, prered;
-  assert(x.size()==n); // check the caller is not cheating us
+  eigen_assert(x.size()==n); // check the caller is not cheating us
 
   temp = 0.0; xnorm = 0.0;
   /* calculate the jacobian matrix. */
@@ -176,4 +176,4 @@ LevenbergMarquardt<FunctorType>::minimizeOneStep(FVectorType  &x)
   
 } // end namespace Eigen
 
-#endif // EIGEN_LMONESTEP_H
\ No newline at end of file
+#endif // EIGEN_LMONESTEP_H
diff --git a/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h b/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h
index dc60ca05a..af76a9799 100644
--- a/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h
+++ b/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h
@@ -45,8 +45,8 @@ namespace internal {
     /* Function Body */
     const Scalar dwarf = (std::numeric_limits<Scalar>::min)();
     const Index n = qr.matrixQR().cols();
-    assert(n==diag.size());
-    assert(n==qtb.size());
+    eigen_assert(n==diag.size());
+    eigen_assert(n==qtb.size());
 
     VectorType  wa1, wa2;
 
diff --git a/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h b/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h
index e45e73ab5..bf2423ef0 100644
--- a/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h
+++ b/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h
@@ -257,7 +257,7 @@ LevenbergMarquardt<FunctorType>::minimizeInit(FVectorType  &x)
 //     m_fjac.reserve(VectorXi::Constant(n,5)); // FIXME Find a better alternative
     if (!m_useExternalScaling)
         m_diag.resize(n);
-    assert( (!m_useExternalScaling || m_diag.size()==n) || "When m_useExternalScaling is set, the caller must provide a valid 'm_diag'");
+    eigen_assert( (!m_useExternalScaling || m_diag.size()==n) || "When m_useExternalScaling is set, the caller must provide a valid 'm_diag'");
     m_qtf.resize(n);
 
     /* Function Body */
diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h b/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h
index 166393f00..b00e5e921 100644
--- a/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h
+++ b/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h
@@ -237,7 +237,7 @@ void MatrixLogarithmAtomic<MatrixType>::computePade3(MatrixType& result, const M
             0.8872983346207416885179265399782400L };
   const RealScalar weights[] = { 0.2777777777777777777777777777777778L, 0.4444444444444444444444444444444444L,
             0.2777777777777777777777777777777778L };
-  assert(degree <= maxPadeDegree);
+  eigen_assert(degree <= maxPadeDegree);
   MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows());
   result.setZero(T.rows(), T.rows());
   for (int k = 0; k < degree; ++k)
@@ -253,7 +253,7 @@ void MatrixLogarithmAtomic<MatrixType>::computePade4(MatrixType& result, const M
             0.6699905217924281324013328795516223L, 0.9305681557970262876119732444464048L };
   const RealScalar weights[] = { 0.1739274225687269286865319746109997L, 0.3260725774312730713134680253890003L,
             0.3260725774312730713134680253890003L, 0.1739274225687269286865319746109997L };
-  assert(degree <= maxPadeDegree);
+  eigen_assert(degree <= maxPadeDegree);
   MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows());
   result.setZero(T.rows(), T.rows());
   for (int k = 0; k < degree; ++k)
@@ -271,7 +271,7 @@ void MatrixLogarithmAtomic<MatrixType>::computePade5(MatrixType& result, const M
   const RealScalar weights[] = { 0.1184634425280945437571320203599587L, 0.2393143352496832340206457574178191L,
             0.2844444444444444444444444444444444L, 0.2393143352496832340206457574178191L,
             0.1184634425280945437571320203599587L };
-  assert(degree <= maxPadeDegree);
+  eigen_assert(degree <= maxPadeDegree);
   MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows());
   result.setZero(T.rows(), T.rows());
   for (int k = 0; k < degree; ++k)
@@ -289,7 +289,7 @@ void MatrixLogarithmAtomic<MatrixType>::computePade6(MatrixType& result, const M
   const RealScalar weights[] = { 0.0856622461895851725201480710863665L, 0.1803807865240693037849167569188581L,
             0.2339569672863455236949351719947755L, 0.2339569672863455236949351719947755L,
             0.1803807865240693037849167569188581L, 0.0856622461895851725201480710863665L };
-  assert(degree <= maxPadeDegree);
+  eigen_assert(degree <= maxPadeDegree);
   MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows());
   result.setZero(T.rows(), T.rows());
   for (int k = 0; k < degree; ++k)
@@ -309,7 +309,7 @@ void MatrixLogarithmAtomic<MatrixType>::computePade7(MatrixType& result, const M
             0.1909150252525594724751848877444876L, 0.2089795918367346938775510204081633L,
             0.1909150252525594724751848877444876L, 0.1398526957446383339507338857118898L,
             0.0647424830844348466353057163395410L };
-  assert(degree <= maxPadeDegree);
+  eigen_assert(degree <= maxPadeDegree);
   MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows());
   result.setZero(T.rows(), T.rows());
   for (int k = 0; k < degree; ++k)
@@ -329,7 +329,7 @@ void MatrixLogarithmAtomic<MatrixType>::computePade8(MatrixType& result, const M
             0.1568533229389436436689811009933007L, 0.1813418916891809914825752246385978L,
             0.1813418916891809914825752246385978L, 0.1568533229389436436689811009933007L,
             0.1111905172266872352721779972131204L, 0.0506142681451881295762656771549811L };
-  assert(degree <= maxPadeDegree);
+  eigen_assert(degree <= maxPadeDegree);
   MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows());
   result.setZero(T.rows(), T.rows());
   for (int k = 0; k < degree; ++k)
@@ -351,7 +351,7 @@ void MatrixLogarithmAtomic<MatrixType>::computePade9(MatrixType& result, const M
             0.1651196775006298815822625346434870L, 0.1561735385200014200343152032922218L,
             0.1303053482014677311593714347093164L, 0.0903240803474287020292360156214564L,
             0.0406371941807872059859460790552618L };
-  assert(degree <= maxPadeDegree);
+  eigen_assert(degree <= maxPadeDegree);
   MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows());
   result.setZero(T.rows(), T.rows());
   for (int k = 0; k < degree; ++k)
@@ -373,7 +373,7 @@ void MatrixLogarithmAtomic<MatrixType>::computePade10(MatrixType& result, const
             0.1477621123573764350869464973256692L, 0.1477621123573764350869464973256692L,
             0.1346333596549981775456134607847347L, 0.1095431812579910219977674671140816L,
             0.0747256745752902965728881698288487L, 0.0333356721543440687967844049466659L };
-  assert(degree <= maxPadeDegree);
+  eigen_assert(degree <= maxPadeDegree);
   MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows());
   result.setZero(T.rows(), T.rows());
   for (int k = 0; k < degree; ++k)
@@ -397,7 +397,7 @@ void MatrixLogarithmAtomic<MatrixType>::computePade11(MatrixType& result, const
             0.1314022722551233310903444349452546L, 0.1165968822959952399592618524215876L,
             0.0931451054638671257130488207158280L, 0.0627901847324523123173471496119701L,
             0.0278342835580868332413768602212743L };
-  assert(degree <= maxPadeDegree);
+  eigen_assert(degree <= maxPadeDegree);
   MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows());
   result.setZero(T.rows(), T.rows());
   for (int k = 0; k < degree; ++k)
diff --git a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
index b190827b3..5b24b4619 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
@@ -150,7 +150,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveInit(FVectorType  &x)
     fjac.resize(n, n);
     if (!useExternalScaling)
         diag.resize(n);
-    assert( (!useExternalScaling || diag.size()==n) || "When useExternalScaling is set, the caller must provide a valid 'diag'");
+    eigen_assert( (!useExternalScaling || diag.size()==n) || "When useExternalScaling is set, the caller must provide a valid 'diag'");
 
     /* Function Body */
     nfev = 0;
@@ -187,7 +187,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType  &x)
 {
     using std::abs;
     
-    assert(x.size()==n); // check the caller is not cheating us
+    eigen_assert(x.size()==n); // check the caller is not cheating us
 
     Index j;
     std::vector<JacobiRotation<Scalar> > v_givens(n), w_givens(n);
@@ -390,7 +390,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(FVectorType  &
     fvec.resize(n);
     if (!useExternalScaling)
         diag.resize(n);
-    assert( (!useExternalScaling || diag.size()==n) || "When useExternalScaling is set, the caller must provide a valid 'diag'");
+    eigen_assert( (!useExternalScaling || diag.size()==n) || "When useExternalScaling is set, the caller must provide a valid 'diag'");
 
     /* Function Body */
     nfev = 0;
diff --git a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
index 4b1a2d0fb..3d0a9c8fc 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
@@ -172,7 +172,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(FVectorType  &x)
     fjac.resize(m, n);
     if (!useExternalScaling)
         diag.resize(n);
-    assert( (!useExternalScaling || diag.size()==n) || "When useExternalScaling is set, the caller must provide a valid 'diag'");
+    eigen_assert( (!useExternalScaling || diag.size()==n) || "When useExternalScaling is set, the caller must provide a valid 'diag'");
     qtf.resize(n);
 
     /* Function Body */
@@ -209,7 +209,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType  &x)
     using std::abs;
     using std::sqrt;
     
-    assert(x.size()==n); // check the caller is not cheating us
+    eigen_assert(x.size()==n); // check the caller is not cheating us
 
     /* calculate the jacobian matrix. */
     Index df_ret = functor.df(x, fjac);
@@ -391,7 +391,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(FVectorType
     fjac.resize(n, n);
     if (!useExternalScaling)
         diag.resize(n);
-    assert( (!useExternalScaling || diag.size()==n) || "When useExternalScaling is set, the caller must provide a valid 'diag'");
+    eigen_assert( (!useExternalScaling || diag.size()==n) || "When useExternalScaling is set, the caller must provide a valid 'diag'");
     qtf.resize(n);
 
     /* Function Body */
@@ -429,7 +429,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
     using std::abs;
     using std::sqrt;
     
-    assert(x.size()==n); // check the caller is not cheating us
+    eigen_assert(x.size()==n); // check the caller is not cheating us
 
     Index i, j;
     bool sing;
diff --git a/unsupported/Eigen/src/NonLinearOptimization/covar.h b/unsupported/Eigen/src/NonLinearOptimization/covar.h
index c2fb79441..68260d191 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/covar.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/covar.h
@@ -20,7 +20,7 @@ void covar(
     const Index n = r.cols();
     const Scalar tolr = tol * abs(r(0,0));
     Matrix< Scalar, Dynamic, 1 > wa(n);
-    assert(ipvt.size()==n);
+    eigen_assert(ipvt.size()==n);
 
     /* form the inverse of r in the full upper triangle of r. */
     l = -1;
diff --git a/unsupported/Eigen/src/NonLinearOptimization/dogleg.h b/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
index 57dbc8bfb..4210958e7 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
@@ -24,9 +24,9 @@ void dogleg(
     /* Function Body */
     const Scalar epsmch = NumTraits<Scalar>::epsilon();
     const Index n = qrfac.cols();
-    assert(n==qtb.size());
-    assert(n==x.size());
-    assert(n==diag.size());
+    eigen_assert(n==qtb.size());
+    eigen_assert(n==x.size());
+    eigen_assert(n==diag.size());
     Matrix< Scalar, Dynamic, 1 >  wa1(n), wa2(n);
 
     /* first, calculate the gauss-newton direction. */
diff --git a/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h b/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
index 05947936e..bb7cf267b 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
@@ -27,7 +27,7 @@ DenseIndex fdjac1(
     /* Function Body */
     const Scalar epsmch = NumTraits<Scalar>::epsilon();
     const Index n = x.size();
-    assert(fvec.size()==n);
+    eigen_assert(fvec.size()==n);
     Matrix< Scalar, Dynamic, 1 >  wa1(n);
     Matrix< Scalar, Dynamic, 1 >  wa2(n);
 
diff --git a/unsupported/Eigen/src/NonLinearOptimization/lmpar.h b/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
index 834407c5a..4c17d4cdf 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
@@ -29,9 +29,9 @@ void lmpar(
     /* Function Body */
     const Scalar dwarf = (std::numeric_limits<Scalar>::min)();
     const Index n = r.cols();
-    assert(n==diag.size());
-    assert(n==qtb.size());
-    assert(n==x.size());
+    eigen_assert(n==diag.size());
+    eigen_assert(n==qtb.size());
+    eigen_assert(n==x.size());
 
     Matrix< Scalar, Dynamic, 1 >  wa1, wa2;
 
@@ -187,8 +187,8 @@ void lmpar2(
     /* Function Body */
     const Scalar dwarf = (std::numeric_limits<Scalar>::min)();
     const Index n = qr.matrixQR().cols();
-    assert(n==diag.size());
-    assert(n==qtb.size());
+    eigen_assert(n==diag.size());
+    eigen_assert(n==qtb.size());
 
     Matrix< Scalar, Dynamic, 1 >  wa1, wa2;
 
diff --git a/unsupported/Eigen/src/NonLinearOptimization/r1updt.h b/unsupported/Eigen/src/NonLinearOptimization/r1updt.h
index 55fae5ae8..f28766061 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/r1updt.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/r1updt.h
@@ -24,10 +24,10 @@ void r1updt(
 
     // r1updt had a broader usecase, but we dont use it here. And, more
     // importantly, we can not test it.
-    assert(m==n);
-    assert(u.size()==m);
-    assert(v.size()==n);
-    assert(w.size()==n);
+    eigen_assert(m==n);
+    eigen_assert(u.size()==m);
+    eigen_assert(v.size()==n);
+    eigen_assert(w.size()==n);
 
     /* move the nontrivial part of the last column of s into w. */
     w[n-1] = s(n-1,n-1);
diff --git a/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h b/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h
index 9ce079e22..6ebf8563f 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h
@@ -12,7 +12,7 @@ void rwupdt(
     typedef DenseIndex Index;
 
     const Index n = r.cols();
-    assert(r.rows()>=n);
+    eigen_assert(r.rows()>=n);
     std::vector<JacobiRotation<Scalar> > givens(n);
 
     /* Local variables */
diff --git a/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h b/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
index 7ee30e18c..ea5d8bc27 100644
--- a/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
+++ b/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
@@ -86,7 +86,7 @@ public:
                 // do nothing
                 break;
             default:
-                assert(false);
+                eigen_assert(false);
         };
 
         // Function Body
@@ -112,7 +112,7 @@ public:
                     jac.col(j) = (val2-val1)/(2*h);
                     break;
                 default:
-                    assert(false);
+                    eigen_assert(false);
             };
         }
         return nfev;
diff --git a/unsupported/Eigen/src/Polynomials/PolynomialSolver.h b/unsupported/Eigen/src/Polynomials/PolynomialSolver.h
index fba8fc910..ad486f08e 100644
--- a/unsupported/Eigen/src/Polynomials/PolynomialSolver.h
+++ b/unsupported/Eigen/src/Polynomials/PolynomialSolver.h
@@ -344,7 +344,7 @@ class PolynomialSolver : public PolynomialSolverBase<_Scalar,_Deg>
     template< typename OtherPolynomial >
     void compute( const OtherPolynomial& poly )
     {
-      assert( Scalar(0) != poly[poly.size()-1] );
+      eigen_assert( Scalar(0) != poly[poly.size()-1] );
       internal::companion<Scalar,_Deg> companion( poly );
       companion.balance();
       m_eigenSolver.compute( companion.denseMatrix() );
@@ -376,7 +376,7 @@ class PolynomialSolver<_Scalar,1> : public PolynomialSolverBase<_Scalar,1>
     template< typename OtherPolynomial >
     void compute( const OtherPolynomial& poly )
     {
-      assert( Scalar(0) != poly[poly.size()-1] );
+      eigen_assert( Scalar(0) != poly[poly.size()-1] );
       m_roots[0] = -poly[0]/poly[poly.size()-1];
     }
 
diff --git a/unsupported/Eigen/src/Polynomials/PolynomialUtils.h b/unsupported/Eigen/src/Polynomials/PolynomialUtils.h
index 5a9ab110e..27d4e9f91 100644
--- a/unsupported/Eigen/src/Polynomials/PolynomialUtils.h
+++ b/unsupported/Eigen/src/Polynomials/PolynomialUtils.h
@@ -78,7 +78,7 @@ typename NumTraits<typename Polynomial::Scalar>::Real cauchy_max_bound( const Po
   typedef typename Polynomial::Scalar Scalar;
   typedef typename NumTraits<Scalar>::Real Real;
 
-  assert( Scalar(0) != poly[poly.size()-1] );
+  eigen_assert( Scalar(0) != poly[poly.size()-1] );
   const Scalar inv_leading_coeff = Scalar(1)/poly[poly.size()-1];
   Real cb(0);
 
diff --git a/unsupported/Eigen/src/SparseExtra/MarketIO.h b/unsupported/Eigen/src/SparseExtra/MarketIO.h
index de958de9f..7aafce928 100644
--- a/unsupported/Eigen/src/SparseExtra/MarketIO.h
+++ b/unsupported/Eigen/src/SparseExtra/MarketIO.h
@@ -116,7 +116,7 @@ inline bool getMarketHeader(const std::string& filename, int& sym, bool& iscompl
   
   std::string line; 
   // The matrix header is always the first line in the file 
-  std::getline(in, line); assert(in.good());
+  std::getline(in, line); eigen_assert(in.good());
   
   std::stringstream fmtline(line); 
   std::string substr[5];
@@ -200,11 +200,11 @@ bool loadMarketVector(VectorType& vec, const std::string& filename)
   int n(0), col(0); 
   do 
   { // Skip comments
-    std::getline(in, line); assert(in.good());
+    std::getline(in, line); eigen_assert(in.good());
   } while (line[0] == '%');
   std::istringstream newline(line);
   newline  >> n >> col; 
-  assert(n>0 && col>0);
+  eigen_assert(n>0 && col>0);
   vec.resize(n);
   int i = 0; 
   Scalar value;