protect calls to min and max with parentheses to make Eigen compatible with default windows.h

(transplanted from 49b6e9143e1d74441924c0c313536e263e12a55c
)

5 files changed, 30 insertions, 30 deletions

diff --git a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
index 5327d5901..f1fdf50b9 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
@@ -255,7 +255,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType  &x)
 
         /* on the first iteration, adjust the initial step bound. */
         if (iter == 1)
-            delta = std::min(delta,pnorm);
+            delta = (std::min)(delta,pnorm);
 
         /* evaluate the function at x + p and calculate its norm. */
         if ( functor(wa2, wa4) < 0)
@@ -289,7 +289,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType  &x)
             ncfail = 0;
             ++ncsuc;
             if (ratio >= Scalar(.5) || ncsuc > 1)
-                delta = std::max(delta, pnorm / Scalar(.5));
+                delta = (std::max)(delta, pnorm / Scalar(.5));
             if (internal::abs(ratio - 1.) <= Scalar(.1)) {
                 delta = pnorm / Scalar(.5);
             }
@@ -322,7 +322,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType  &x)
         /* tests for termination and stringent tolerances. */
         if (nfev >= parameters.maxfev)
             return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
-        if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
+        if (Scalar(.1) * (std::max)(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
             return HybridNonLinearSolverSpace::TolTooSmall;
         if (nslow2 == 5)
             return HybridNonLinearSolverSpace::NotMakingProgressJacobian;
@@ -449,7 +449,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
     /* calculate the jacobian matrix. */
     if (internal::fdjac1(functor, x, fvec, fjac, parameters.nb_of_subdiagonals, parameters.nb_of_superdiagonals, parameters.epsfcn) <0)
         return HybridNonLinearSolverSpace::UserAsked;
-    nfev += std::min(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
+    nfev += (std::min)(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
 
     wa2 = fjac.colwise().blueNorm();
 
@@ -496,7 +496,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
 
         /* on the first iteration, adjust the initial step bound. */
         if (iter == 1)
-            delta = std::min(delta,pnorm);
+            delta = (std::min)(delta,pnorm);
 
         /* evaluate the function at x + p and calculate its norm. */
         if ( functor(wa2, wa4) < 0)
@@ -530,7 +530,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
             ncfail = 0;
             ++ncsuc;
             if (ratio >= Scalar(.5) || ncsuc > 1)
-                delta = std::max(delta, pnorm / Scalar(.5));
+                delta = (std::max)(delta, pnorm / Scalar(.5));
             if (internal::abs(ratio - 1.) <= Scalar(.1)) {
                 delta = pnorm / Scalar(.5);
             }
@@ -563,7 +563,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
         /* tests for termination and stringent tolerances. */
         if (nfev >= parameters.maxfev)
             return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
-        if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
+        if (Scalar(.1) * (std::max)(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
             return HybridNonLinearSolverSpace::TolTooSmall;
         if (nslow2 == 5)
             return HybridNonLinearSolverSpace::NotMakingProgressJacobian;
diff --git a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
index c43df3f7a..0ae681b1c 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
@@ -263,7 +263,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType  &x)
     if (fnorm != 0.)
         for (Index j = 0; j < n; ++j)
             if (wa2[permutation.indices()[j]] != 0.)
-                gnorm = std::max(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
+                gnorm = (std::max)(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
 
     /* test for convergence of the gradient norm. */
     if (gnorm <= parameters.gtol)
@@ -285,7 +285,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType  &x)
 
         /* on the first iteration, adjust the initial step bound. */
         if (iter == 1)
-            delta = std::min(delta,pnorm);
+            delta = (std::min)(delta,pnorm);
 
         /* evaluate the function at x + p and calculate its norm. */
         if ( functor(wa2, wa4) < 0)
@@ -321,7 +321,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType  &x)
             if (Scalar(.1) * fnorm1 >= fnorm || temp < Scalar(.1))
                 temp = Scalar(.1);
             /* Computing MIN */
-            delta = temp * std::min(delta, pnorm / Scalar(.1));
+            delta = temp * (std::min)(delta, pnorm / Scalar(.1));
             par /= temp;
         } else if (!(par != 0. && ratio < Scalar(.75))) {
             delta = pnorm / Scalar(.5);
@@ -510,7 +510,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
     if (fnorm != 0.)
         for (j = 0; j < n; ++j)
             if (wa2[permutation.indices()[j]] != 0.)
-                gnorm = std::max(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
+                gnorm = (std::max)(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
 
     /* test for convergence of the gradient norm. */
     if (gnorm <= parameters.gtol)
@@ -532,7 +532,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
 
         /* on the first iteration, adjust the initial step bound. */
         if (iter == 1)
-            delta = std::min(delta,pnorm);
+            delta = (std::min)(delta,pnorm);
 
         /* evaluate the function at x + p and calculate its norm. */
         if ( functor(wa2, wa4) < 0)
@@ -568,7 +568,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
             if (Scalar(.1) * fnorm1 >= fnorm || temp < Scalar(.1))
                 temp = Scalar(.1);
             /* Computing MIN */
-            delta = temp * std::min(delta, pnorm / Scalar(.1));
+            delta = temp * (std::min)(delta, pnorm / Scalar(.1));
             par /= temp;
         } else if (!(par != 0. && ratio < Scalar(.75))) {
             delta = pnorm / Scalar(.5);
diff --git a/unsupported/Eigen/src/NonLinearOptimization/dogleg.h b/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
index fffd9e0be..cbdcf4b71 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
@@ -93,7 +93,7 @@ algo_end:
 
     /* form appropriate convex combination of the gauss-newton */
     /* direction and the scaled gradient direction. */
-    temp = (1.-alpha) * std::min(sgnorm,delta);
+    temp = (1.-alpha) * (std::min)(sgnorm,delta);
     x = temp * wa1 + alpha * x;
 }
 
diff --git a/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h b/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
index 887b76fa1..0a26c2061 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
@@ -26,7 +26,7 @@ DenseIndex fdjac1(
     Matrix< Scalar, Dynamic, 1 >  wa1(n);
     Matrix< Scalar, Dynamic, 1 >  wa2(n);
 
-    eps = sqrt(std::max(epsfcn,epsmch));
+    eps = sqrt((std::max)(epsfcn,epsmch));
     msum = ml + mu + 1;
     if (msum >= n) {
         /* computation of dense approximate jacobian. */
@@ -61,7 +61,7 @@ DenseIndex fdjac1(
                 if (h == 0.) h = eps;
                 fjac.col(j).setZero();
                 start = std::max<Index>(0,j-mu);
-                length = std::min(n-1, j+ml) - start + 1;
+                length = (std::min)(n-1, j+ml) - start + 1;
                 fjac.col(j).segment(start, length) = ( wa1.segment(start, length)-fvec.segment(start, length))/h;
             }
         }
diff --git a/unsupported/Eigen/src/NonLinearOptimization/lmpar.h b/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
index a6bbc50ba..62f4aabc9 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
@@ -91,12 +91,12 @@ void lmpar(
     gnorm = wa1.stableNorm();
     paru = gnorm / delta;
     if (paru == 0.)
-        paru = dwarf / std::min(delta,Scalar(0.1));
+        paru = dwarf / (std::min)(delta,Scalar(0.1));
 
     /* if the input par lies outside of the interval (parl,paru), */
     /* set par to the closer endpoint. */
-    par = std::max(par,parl);
-    par = std::min(par,paru);
+    par = (std::max)(par,parl);
+    par = (std::min)(par,paru);
     if (par == 0.)
         par = gnorm / dxnorm;
 
@@ -106,7 +106,7 @@ void lmpar(
 
         /* evaluate the function at the current value of par. */
         if (par == 0.)
-            par = std::max(dwarf,Scalar(.001) * paru); /* Computing MAX */
+            par = (std::max)(dwarf,Scalar(.001) * paru); /* Computing MAX */
         wa1 = sqrt(par)* diag;
 
         Matrix< Scalar, Dynamic, 1 > sdiag(n);
@@ -139,13 +139,13 @@ void lmpar(
 
         /* depending on the sign of the function, update parl or paru. */
         if (fp > 0.)
-            parl = std::max(parl,par);
+            parl = (std::max)(parl,par);
         if (fp < 0.)
-            paru = std::min(paru,par);
+            paru = (std::min)(paru,par);
 
         /* compute an improved estimate for par. */
         /* Computing MAX */
-        par = std::max(parl,par+parc);
+        par = (std::max)(parl,par+parc);
 
         /* end of an iteration. */
     }
@@ -227,12 +227,12 @@ void lmpar2(
     gnorm = wa1.stableNorm();
     paru = gnorm / delta;
     if (paru == 0.)
-        paru = dwarf / std::min(delta,Scalar(0.1));
+        paru = dwarf / (std::min)(delta,Scalar(0.1));
 
     /* if the input par lies outside of the interval (parl,paru), */
     /* set par to the closer endpoint. */
-    par = std::max(par,parl);
-    par = std::min(par,paru);
+    par = (std::max)(par,parl);
+    par = (std::min)(par,paru);
     if (par == 0.)
         par = gnorm / dxnorm;
 
@@ -243,7 +243,7 @@ void lmpar2(
 
         /* evaluate the function at the current value of par. */
         if (par == 0.)
-            par = std::max(dwarf,Scalar(.001) * paru); /* Computing MAX */
+            par = (std::max)(dwarf,Scalar(.001) * paru); /* Computing MAX */
         wa1 = sqrt(par)* diag;
 
         Matrix< Scalar, Dynamic, 1 > sdiag(n);
@@ -275,12 +275,12 @@ void lmpar2(
 
         /* depending on the sign of the function, update parl or paru. */
         if (fp > 0.)
-            parl = std::max(parl,par);
+            parl = (std::max)(parl,par);
         if (fp < 0.)
-            paru = std::min(paru,par);
+            paru = (std::min)(paru,par);
 
         /* compute an improved estimate for par. */
-        par = std::max(parl,par+parc);
+        par = (std::max)(parl,par+parc);
     }
     if (iter == 0)
         par = 0.;