diff --git a/CMakeLists.txt b/CMakeLists.txt
index 501a2de3..999c8fea 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -21,6 +21,10 @@ FILE(APPEND THCUNN_generic_h.lua "]]")
 
 FILE(GLOB luasrc *.lua)
 
+IF (ANDROID)
+ ADD_DEFINITIONS(-DTHC_MIN_MATH)
+ENDIF()
+
 ADD_SUBDIRECTORY(lib)
 
 INSTALL(
diff --git a/THCUNN.lua b/THCUNN.lua
index 6776a238..9dd90e5d 100644
--- a/THCUNN.lua
+++ b/THCUNN.lua
@@ -129,8 +129,10 @@ local function_names_generic = extract_function_names_generic(THCUNN_generic_h)
 THNN.kernels['torch.CudaTensor'] = THNN.bind(THCUNN.C, function_names_generic, 'Cuda', THCUNN.getState)
 torch.getmetatable('torch.CudaTensor').THNN = THNN.kernels['torch.CudaTensor']
 
-THNN.kernels['torch.CudaDoubleTensor'] = THNN.bind(THCUNN.C, function_names_generic, 'CudaDouble', THCUNN.getState)
-torch.getmetatable('torch.CudaDoubleTensor').THNN = THNN.kernels['torch.CudaDoubleTensor']
+if not cutorch.minMath then
+   THNN.kernels['torch.CudaDoubleTensor'] = THNN.bind(THCUNN.C, function_names_generic, 'CudaDouble', THCUNN.getState)
+   torch.getmetatable('torch.CudaDoubleTensor').THNN = THNN.kernels['torch.CudaDoubleTensor']
+end
 
 if cutorch.hasHalf then
 -- in order to call 'half' functions from lua, convert real arguments from
@@ -164,7 +166,10 @@ local function Module__converter(type)
     end
 end
 
-rawset(torch.getmetatable('nn.Module'), 'cudaDouble', Module__converter('torch.CudaDoubleTensor'))
+if not cutorch.minMath then
+   rawset(torch.getmetatable('nn.Module'), 'cudaDouble', Module__converter('torch.CudaDoubleTensor'))
+end
+
 if cutorch.hasHalf then
     rawset(torch.getmetatable('nn.Module'), 'cudaHalf', Module__converter('torch.CudaHalfTensor'))
 end
diff --git a/init.lua b/init.lua
index fd1e319b..6a1ceb6b 100644
--- a/init.lua
+++ b/init.lua
@@ -9,5 +9,4 @@ require('cunn.DataParallelTable')
 
 nn.Module._flattenTensorBuffer['torch.CudaTensor'] = torch.FloatTensor.new
 nn.Module._flattenTensorBuffer['torch.CudaDoubleTensor'] = torch.DoubleTensor.new
--- FIXME: change this to torch.HalfTensor when available
-nn.Module._flattenTensorBuffer['torch.CudaHalfTensor'] = torch.FloatTensor.new
+nn.Module._flattenTensorBuffer['torch.CudaHalfTensor'] = torch.HalfTensor.new
diff --git a/lib/THCUNN/SparseLinear.cu b/lib/THCUNN/SparseLinear.cu
index a7ffa1e2..b1b756ac 100644
--- a/lib/THCUNN/SparseLinear.cu
+++ b/lib/THCUNN/SparseLinear.cu
@@ -82,6 +82,5 @@ void THNN_CudaHalfSparseLinear_updateParameters(
 #endif
 
 #include "generic/SparseLinear.cu"
-#include "THCGenerateFloatType.h"
-#include "generic/SparseLinear.cu"
-#include "THCGenerateDoubleType.h"
+#include "THCGenerateFloatTypes.h"
+
diff --git a/lib/THCUNN/THCGenerateFloatTypes.h b/lib/THCUNN/THCGenerateFloatTypes.h
new file mode 100644
index 00000000..a197eed7
--- /dev/null
+++ b/lib/THCUNN/THCGenerateFloatTypes.h
@@ -0,0 +1,35 @@
+#ifndef THC_GENERIC_FILE
+#error "You must define THC_GENERIC_FILE before including THGenerateFloatTypes.h"
+#endif
+
+#define THCGenerateFloatTypes
+
+#define THCTypeIdxByte   1
+#define THCTypeIdxChar   2
+#define THCTypeIdxShort  3
+#define THCTypeIdxInt    4
+#define THCTypeIdxLong   5
+#define THCTypeIdxFloat  6
+#define THCTypeIdxDouble 7
+#define THCTypeIdxHalf   8
+#define THCTypeIdx_(T) TH_CONCAT_2(THCTypeIdx,T)
+
+# ifndef THC_MIN_MATH
+#  include "THCGenerateHalfType.h"
+#  include "THCGenerateDoubleType.h"
+# endif
+
+#include "THCGenerateFloatType.h"
+
+#undef THCTypeIdxByte
+#undef THCTypeIdxChar
+#undef THCTypeIdxShort
+#undef THCTypeIdxInt
+#undef THCTypeIdxLong
+#undef THCTypeIdxFloat
+#undef THCTypeIdxDouble
+#undef THCTypeIdxHalf
+#undef THCTypeIdx_
+
+#undef THCGenerateFloatTypes
+#undef THC_GENERIC_FILE
diff --git a/test.lua b/test.lua
index c3ed9bb2..4a137232 100644
--- a/test.lua
+++ b/test.lua
@@ -11,20 +11,21 @@ local THC = ffi.os == 'Windows' and ffi.load('THC') or ffi.C
 --e.g.: th -lcunn -e "nn.testcuda{'Sigmoid_forward'}"
 
 local typenames = {
-  'torch.CudaTensor',
-  'torch.CudaDoubleTensor',
+  'torch.CudaTensor'
 }
 
 local t2cpu = {
-  ['torch.CudaTensor'] = 'torch.FloatTensor',
-  ['torch.CudaDoubleTensor'] = 'torch.DoubleTensor',
-
+  ['torch.CudaTensor'] = 'torch.FloatTensor'
 }
 
 local function checkHalf()
-   if cutorch.hasHalf then
+   if not cutorch.minMath then
+     if cutorch.hasHalf then
        table.insert(typenames, 'torch.CudaHalfTensor')
-       t2cpu['torch.CudaHalfTensor'] = 'torch.FloatTensor'
+       t2cpu['torch.CudaHalfTensor'] = 'torch.HalfTensor'
+     end
+       table.insert(typenames, 'torch.CudaDoubleTensor')
+       t2cpu['torch.CudaDoubleTensor'] = 'torch.DoubleTensor'
    end
 end