diff --git a/src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp b/src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp
index b0c0f9ba93..12d061d53a 100644
--- a/src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp
+++ b/src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp
@@ -106,7 +106,7 @@ class SVDBatchLearning
         const double val = V(i, j);
         if (val != 0)
           deltaW.row(i) += (val - arma::dot(W.row(i), H.col(j))) *
-                                            arma::trans(H.col(j));
+                                            trans(H.col(j));
       }
       // Add regularization.
       if (kw != 0)
@@ -215,7 +215,7 @@ inline void SVDBatchLearning::WUpdate<arma::sp_mat>(const arma::sp_mat& V,
     const size_t row = it.row();
     const size_t col = it.col();
     deltaW.row(it.row()) += (*it - arma::dot(W.row(row), H.col(col))) *
-                                             arma::trans(H.col(col));
+                                             trans(H.col(col));
   }
 
   if (kw != 0)
diff --git a/src/mlpack/methods/amf/update_rules/svd_complete_incremental_learning.hpp b/src/mlpack/methods/amf/update_rules/svd_complete_incremental_learning.hpp
index 994a964aa3..0d83af5e5c 100644
--- a/src/mlpack/methods/amf/update_rules/svd_complete_incremental_learning.hpp
+++ b/src/mlpack/methods/amf/update_rules/svd_complete_incremental_learning.hpp
@@ -217,7 +217,7 @@ class SVDCompleteIncrementalLearning<arma::sp_mat>
     deltaW.zeros();
 
     deltaW += (**it - arma::dot(W.row(currentItemIndex),
-        H.col(currentUserIndex))) * arma::trans(H.col(currentUserIndex));
+        H.col(currentUserIndex))) * trans(H.col(currentUserIndex));
     if (kw != 0) deltaW -= kw * W.row(currentItemIndex);
 
     W.row(currentItemIndex) += u*deltaW;
@@ -243,7 +243,7 @@ class SVDCompleteIncrementalLearning<arma::sp_mat>
     size_t currentItemIndex = it->row();
 
     deltaH += (**it - arma::dot(W.row(currentItemIndex),
-        H.col(currentUserIndex))) * arma::trans(W.row(currentItemIndex));
+        H.col(currentUserIndex))) * trans(W.row(currentItemIndex));
     if (kh != 0) deltaH -= kh * H.col(currentUserIndex);
 
     H.col(currentUserIndex) += u * deltaH;
diff --git a/src/mlpack/methods/amf/update_rules/svd_incomplete_incremental_learning.hpp b/src/mlpack/methods/amf/update_rules/svd_incomplete_incremental_learning.hpp
index 4e82703616..245edd8049 100644
--- a/src/mlpack/methods/amf/update_rules/svd_incomplete_incremental_learning.hpp
+++ b/src/mlpack/methods/amf/update_rules/svd_incomplete_incremental_learning.hpp
@@ -171,7 +171,7 @@ inline void SVDIncompleteIncrementalLearning::WUpdate<arma::sp_mat>(
     double val = *it;
     size_t i = it.row();
     deltaW.row(i) += (val - arma::dot(W.row(i), H.col(currentUserIndex))) *
-        arma::trans(H.col(currentUserIndex));
+        trans(H.col(currentUserIndex));
     if (kw != 0) deltaW.row(i) -= kw * W.row(i);
   }
 
@@ -193,7 +193,7 @@ inline void SVDIncompleteIncrementalLearning::HUpdate<arma::sp_mat>(
     if ((val = V(i, currentUserIndex)) != 0)
     {
       deltaH += (val - arma::dot(W.row(i), H.col(currentUserIndex))) *
-          arma::trans(W.row(i));
+          trans(W.row(i));
     }
   }
   if (kh != 0) deltaH -= kh * H.col(currentUserIndex);
diff --git a/src/mlpack/methods/ann/layer/multihead_attention_impl.hpp b/src/mlpack/methods/ann/layer/multihead_attention_impl.hpp
index dc521b04e0..f5872188bc 100644
--- a/src/mlpack/methods/ann/layer/multihead_attention_impl.hpp
+++ b/src/mlpack/methods/ann/layer/multihead_attention_impl.hpp
@@ -114,11 +114,11 @@ Forward(const MatType& input, MatType& output)
 
   for (size_t i = 0; i < batchSize; ++i)
   {
-    qProj.slice(i) = arma::trans(
+    qProj.slice(i) = trans(
         queryWt * q.slice(i) + repmat(qBias, 1, tgtSeqLen));
-    kProj.slice(i) = arma::trans(
+    kProj.slice(i) = trans(
         keyWt * k.slice(i) + repmat(kBias, 1, srcSeqLen));
-    vProj.slice(i) = arma::trans(
+    vProj.slice(i) = trans(
         valueWt * v.slice(i) + repmat(vBias, 1, srcSeqLen));
   }
 
@@ -175,7 +175,7 @@ Forward(const MatType& input, MatType& output)
   // The final output is the linear projection of attention output.
   for (size_t i = 0; i < batchSize; ++i)
   {
-    output.col(i) = vectorise(arma::trans(attnOut.slice(i) * outWt
+    output.col(i) = vectorise(trans(attnOut.slice(i) * outWt
         + repmat(outBias, tgtSeqLen, 1)));
   }
 }
@@ -229,12 +229,12 @@ Backward(const MatType& /* input */,
     if (selfAttention)
     {
       g.submat(0, i, g.n_rows - 1, i) =
-          vectorise(arma::trans(tmp.slice(i) * valueWt));
+          vectorise(trans(tmp.slice(i) * valueWt));
     }
     else
     {
       g.submat((tgtSeqLen + srcSeqLen) * embedDim, i, g.n_rows - 1, i) =
-          vectorise(arma::trans(tmp.slice(i) * valueWt));
+          vectorise(trans(tmp.slice(i) * valueWt));
     }
   }
 
@@ -264,13 +264,13 @@ Backward(const MatType& /* input */,
     {
       // Sum the query, key, and value deltas.
       g.submat(0, i, g.n_rows - 1, i) +=
-          vectorise(arma::trans(tmp.slice(i) * keyWt));
+          vectorise(trans(tmp.slice(i) * keyWt));
     }
     else
     {
       g.submat(tgtSeqLen * embedDim, i,
                (tgtSeqLen + srcSeqLen) * embedDim - 1, i) =
-          vectorise(arma::trans(tmp.slice(i) * keyWt));
+          vectorise(trans(tmp.slice(i) * keyWt));
     }
   }
 
@@ -289,12 +289,12 @@ Backward(const MatType& /* input */,
     {
       // Sum the query, key, and value deltas.
       g.submat(0, i, g.n_rows - 1, i) +=
-          vectorise(arma::trans(tmp.slice(i) * queryWt));
+          vectorise(trans(tmp.slice(i) * queryWt));
     }
     else
     {
       g.submat(0, i, tgtSeqLen * embedDim - 1, i) =
-          vectorise(arma::trans(tmp.slice(i) * queryWt));
+          vectorise(trans(tmp.slice(i) * queryWt));
     }
   }
 }
diff --git a/src/mlpack/methods/ann/layer/not_adapted/glimpse_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/glimpse_impl.hpp
index 5ad2fc4877..8a1207ae61 100644
--- a/src/mlpack/methods/ann/layer/not_adapted/glimpse_impl.hpp
+++ b/src/mlpack/methods/ann/layer/not_adapted/glimpse_impl.hpp
@@ -117,7 +117,7 @@ void GlimpseType<InputType, OutputType>::Forward(
 
   for (size_t i = 0; i < outputTemp.n_slices; ++i)
   {
-    outputTemp.slice(i) = arma::trans(outputTemp.slice(i));
+    outputTemp.slice(i) = trans(outputTemp.slice(i));
   }
 
   output = OutputType(outputTemp.memptr(), outputTemp.n_elem, 1);
diff --git a/src/mlpack/methods/cf/decomposition_policies/block_krylov_svd_method.hpp b/src/mlpack/methods/cf/decomposition_policies/block_krylov_svd_method.hpp
index 61b3d3f22d..6c2ad9a972 100644
--- a/src/mlpack/methods/cf/decomposition_policies/block_krylov_svd_method.hpp
+++ b/src/mlpack/methods/cf/decomposition_policies/block_krylov_svd_method.hpp
@@ -81,7 +81,7 @@ class BlockKrylovSVDPolicy
     w = w * arma::diagmat(sigma);
 
     // Take transpose of the matrix h as required by CF class.
-    h = arma::trans(h);
+    h = trans(h);
   }
 
   /**
diff --git a/src/mlpack/methods/cf/decomposition_policies/quic_svd_method.hpp b/src/mlpack/methods/cf/decomposition_policies/quic_svd_method.hpp
index 33d6cc76c3..775a7c8803 100644
--- a/src/mlpack/methods/cf/decomposition_policies/quic_svd_method.hpp
+++ b/src/mlpack/methods/cf/decomposition_policies/quic_svd_method.hpp
@@ -81,7 +81,7 @@ class QUIC_SVDPolicy
     w = w * sigma;
 
     // Take transpose of the matrix h as required by CF class.
-    h = arma::trans(h);
+    h = trans(h);
   }
 
   /**
diff --git a/src/mlpack/methods/cf/decomposition_policies/randomized_svd_method.hpp b/src/mlpack/methods/cf/decomposition_policies/randomized_svd_method.hpp
index 4497a66299..d5fee641b5 100644
--- a/src/mlpack/methods/cf/decomposition_policies/randomized_svd_method.hpp
+++ b/src/mlpack/methods/cf/decomposition_policies/randomized_svd_method.hpp
@@ -86,7 +86,7 @@ class RandomizedSVDPolicy
     w = w * arma::diagmat(sigma);
 
     // Take transpose of the matrix h as required by CF class.
-    h = arma::trans(h);
+    h = trans(h);
   }
 
   /**
diff --git a/src/mlpack/methods/cf/svd_wrapper.hpp b/src/mlpack/methods/cf/svd_wrapper.hpp
index 64dbae919d..6a70f1702d 100644
--- a/src/mlpack/methods/cf/svd_wrapper.hpp
+++ b/src/mlpack/methods/cf/svd_wrapper.hpp
@@ -53,7 +53,7 @@ class SVDWrapper
    * @param sigma eigenvalue matrix
    * @param H second unitary matrix
    *
-   * @note V = W * sigma * arma::trans(H)
+   * @note V = W * sigma * trans(H)
    */
   double Apply(const arma::mat& V,
                arma::mat& W,
diff --git a/src/mlpack/methods/cf/svd_wrapper_impl.hpp b/src/mlpack/methods/cf/svd_wrapper_impl.hpp
index f2632592ac..fc9b5207d0 100644
--- a/src/mlpack/methods/cf/svd_wrapper_impl.hpp
+++ b/src/mlpack/methods/cf/svd_wrapper_impl.hpp
@@ -34,7 +34,7 @@ double SVDWrapper<Factorizer>::Apply(const arma::mat& V,
   for (size_t i = 0; i < sigma.n_rows && i < sigma.n_cols; ++i)
     sigma(i, i) = E(i, 0);
 
-  arma::mat V_rec = W * sigma * arma::trans(H);
+  arma::mat V_rec = W * sigma * trans(H);
 
   // return normalized frobenius error
   return arma::norm(V - V_rec, "fro") / arma::norm(V, "fro");
@@ -56,7 +56,7 @@ double SVDWrapper<DummyClass>::Apply(const arma::mat& V,
   for (size_t i = 0; i < sigma.n_rows && i < sigma.n_cols; ++i)
     sigma(i, i) = E(i, 0);
 
-  arma::mat V_rec = W * sigma * arma::trans(H);
+  arma::mat V_rec = W * sigma * trans(H);
 
   // return normalized frobenius error
   return arma::norm(V - V_rec, "fro") / arma::norm(V, "fro");
@@ -94,7 +94,7 @@ double SVDWrapper<Factorizer>::Apply(const arma::mat& V,
   W = W * arma::diagmat(sigma);
 
   // take transpose of the matrix H as required by CF module
-  H = arma::trans(H);
+  H = trans(H);
 
   // reconstruct the matrix
   arma::mat V_rec = W * H;
@@ -135,7 +135,7 @@ double SVDWrapper<DummyClass>::Apply(const arma::mat& V,
   W = W * arma::diagmat(sigma);
 
   // take transpose of the matrix H as required by CF module
-  H = arma::trans(H);
+  H = trans(H);
 
   // reconstruct the matrix
   arma::mat V_rec = W * H;
diff --git a/src/mlpack/methods/linear_regression/linear_regression_impl.hpp b/src/mlpack/methods/linear_regression/linear_regression_impl.hpp
index d4a61f39e2..911bbc69c3 100644
--- a/src/mlpack/methods/linear_regression/linear_regression_impl.hpp
+++ b/src/mlpack/methods/linear_regression/linear_regression_impl.hpp
@@ -273,7 +273,7 @@ inline void LinearRegression<ModelMatType>::Predict(
     // the dataset.
     util::CheckSameDimensionality(points, parameters,
         "LinearRegression::Predict()", "points");
-    predictions = arma::trans(parameters) * points;
+    predictions = trans(parameters) * points;
   }
 }
 
diff --git a/src/mlpack/methods/lmnn/lmnn_function_impl.hpp b/src/mlpack/methods/lmnn/lmnn_function_impl.hpp
index 4d22b87473..6419bf054e 100644
--- a/src/mlpack/methods/lmnn/lmnn_function_impl.hpp
+++ b/src/mlpack/methods/lmnn/lmnn_function_impl.hpp
@@ -580,10 +580,10 @@ void LMNNFunction<MetricType>::Gradient(const arma::mat& transformation,
 
         // Caculate gradient due to impostors.
         arma::vec diff = dataset.col(i) - dataset.col(targetNeighbors(j, i));
-        cil += diff * arma::trans(diff);
+        cil += diff * trans(diff);
 
         diff = dataset.col(i) - dataset.col(impostors(l, i));
-        cil -= diff * arma::trans(diff);
+        cil -= diff * trans(diff);
       }
     }
   }
@@ -654,7 +654,7 @@ void LMNNFunction<MetricType>::Gradient(const arma::mat& transformation,
     {
       // Calculate gradient due to target neighbors.
       arma::vec diff = dataset.col(i) - dataset.col(targetNeighbors(j, i));
-      cij += diff * arma::trans(diff);
+      cij += diff * trans(diff);
     }
 
     for (int j = k - 1; j >= 0; j--)
@@ -718,10 +718,10 @@ void LMNNFunction<MetricType>::Gradient(const arma::mat& transformation,
 
         // Caculate gradient due to impostors.
         arma::vec diff = dataset.col(i) - dataset.col(targetNeighbors(j, i));
-        cil += diff * arma::trans(diff);
+        cil += diff * trans(diff);
 
         diff = dataset.col(i) - dataset.col(impostors(l, i));
-        cil -= diff * arma::trans(diff);
+        cil -= diff * trans(diff);
       }
     }
   }
@@ -857,10 +857,10 @@ double LMNNFunction<MetricType>::EvaluateWithGradient(
 
         // Caculate gradient due to impostors.
         arma::vec diff = dataset.col(i) - dataset.col(targetNeighbors(j, i));
-        cil += diff * arma::trans(diff);
+        cil += diff * trans(diff);
 
         diff = dataset.col(i) - dataset.col(impostors(l, i));
-        cil -= diff * arma::trans(diff);
+        cil -= diff * trans(diff);
       }
     }
   }
@@ -941,7 +941,7 @@ double LMNNFunction<MetricType>::EvaluateWithGradient(
 
       // Calculate gradient due to target neighbors.
       arma::vec diff = dataset.col(i) - dataset.col(targetNeighbors(j, i));
-      cij += diff * arma::trans(diff);
+      cij += diff * trans(diff);
     }
 
     for (int j = k - 1; j >= 0; j--)
@@ -997,10 +997,10 @@ double LMNNFunction<MetricType>::EvaluateWithGradient(
 
         // Caculate gradient due to impostors.
         arma::vec diff = dataset.col(i) - dataset.col(targetNeighbors(j, i));
-        cil += diff * arma::trans(diff);
+        cil += diff * trans(diff);
 
         diff = dataset.col(i) - dataset.col(impostors(l, i));
-        cil -= diff * arma::trans(diff);
+        cil -= diff * trans(diff);
       }
     }
   }
@@ -1025,7 +1025,7 @@ inline void LMNNFunction<MetricType>::Precalculate()
     {
       // Calculate gradient due to target neighbors.
       arma::vec diff = dataset.col(i) - dataset.col(targetNeighbors(j, i));
-      pCij += diff * arma::trans(diff);
+      pCij += diff * trans(diff);
     }
   }
 }
diff --git a/src/mlpack/methods/pca/decomposition_policies/exact_svd_method.hpp b/src/mlpack/methods/pca/decomposition_policies/exact_svd_method.hpp
index c12d7e2e03..4f42d23eac 100644
--- a/src/mlpack/methods/pca/decomposition_policies/exact_svd_method.hpp
+++ b/src/mlpack/methods/pca/decomposition_policies/exact_svd_method.hpp
@@ -66,7 +66,7 @@ class ExactSVDPolicy
     eigVal %= eigVal / (data.n_cols - 1);
 
     // Project the samples to the principals.
-    transformedData = arma::trans(eigvec) * centeredData;
+    transformedData = trans(eigvec) * centeredData;
   }
 };
 
diff --git a/src/mlpack/methods/pca/decomposition_policies/quic_svd_method.hpp b/src/mlpack/methods/pca/decomposition_policies/quic_svd_method.hpp
index c2ab4c6739..4db6aff646 100644
--- a/src/mlpack/methods/pca/decomposition_policies/quic_svd_method.hpp
+++ b/src/mlpack/methods/pca/decomposition_policies/quic_svd_method.hpp
@@ -68,7 +68,7 @@ class QUICSVDPolicy
     eigVal = arma::pow(arma::diagvec(sigma), 2) / (data.n_cols - 1);
 
     // Project the samples to the principals.
-    transformedData = arma::trans(eigvec) * centeredData;
+    transformedData = trans(eigvec) * centeredData;
   }
 
   //! Get the error tolerance fraction for calculated subspace.
diff --git a/src/mlpack/methods/pca/decomposition_policies/randomized_block_krylov_method.hpp b/src/mlpack/methods/pca/decomposition_policies/randomized_block_krylov_method.hpp
index f4e8185c36..7836b72c8a 100644
--- a/src/mlpack/methods/pca/decomposition_policies/randomized_block_krylov_method.hpp
+++ b/src/mlpack/methods/pca/decomposition_policies/randomized_block_krylov_method.hpp
@@ -73,7 +73,7 @@ class RandomizedBlockKrylovSVDPolicy
     eigVal %= eigVal / (data.n_cols - 1);
 
     // Project the samples to the principals.
-    transformedData = arma::trans(eigvec) * centeredData;
+    transformedData = trans(eigvec) * centeredData;
   }
 
   //! Get the number of iterations for the power method.
diff --git a/src/mlpack/methods/pca/decomposition_policies/randomized_svd_method.hpp b/src/mlpack/methods/pca/decomposition_policies/randomized_svd_method.hpp
index e6db69c093..ceb2fab0cd 100644
--- a/src/mlpack/methods/pca/decomposition_policies/randomized_svd_method.hpp
+++ b/src/mlpack/methods/pca/decomposition_policies/randomized_svd_method.hpp
@@ -73,7 +73,7 @@ class RandomizedSVDPCAPolicy
     eigVal %= eigVal / (data.n_cols - 1);
 
     // Project the samples to the principals.
-    transformedData = arma::trans(eigvec) * centeredData;
+    transformedData = trans(eigvec) * centeredData;
   }
 
   //! Get the size of the normalized power iterations.
diff --git a/src/mlpack/methods/randomized_svd/randomized_svd_impl.hpp b/src/mlpack/methods/randomized_svd/randomized_svd_impl.hpp
index f05c03025e..709f0d4841 100644
--- a/src/mlpack/methods/randomized_svd/randomized_svd_impl.hpp
+++ b/src/mlpack/methods/randomized_svd/randomized_svd_impl.hpp
@@ -92,7 +92,7 @@ inline void RandomizedSVD::Apply(const MatType& data,
   if (data.n_cols >= data.n_rows)
   {
     R = arma::randn<arma::mat>(data.n_rows, iteratedPower);
-    Q = (data.t() * R) - repmat(arma::trans(R.t() * rowMean), data.n_cols, 1);
+    Q = (data.t() * R) - repmat(trans(R.t() * rowMean), data.n_cols, 1);
   }
   else
   {