Minor change in backend argument documentation of palm4msa and hierarchical.

wrapper/matlab/+matfaust/+fact/hierarchical.m

@@ -7,7 +7,7 @@
 %> - 'squaremat' to use pre-defined parameters typically used to factorize a Hadamard square matrix of order a power of two (see matfaust.demo.hadamard).
 %> - {'rectmat', j, k, s} to use pre-defined parameters used for instance in factorization of the MEG matrix which is a rectangular matrix of size m*n such that m < n (see matfaust.demo.bsl); j is the number of factors, k the sparsity of the main factor's columns, and s the sparsity of rows for all other factors except the residuum (that is the first factor here because the factorization is made toward the left -- is_side_fact_left == true, cf. matfaust.factparams.ParamsHierarchical).
 %> </br>The residuum has a sparsity of P*rho^(num_facts-1). <br/> By default, rho == .8 and P = 1.4. It's possible to set custom values with for example p == { 'rectmat', j, k, s, 'rho', .4, 'P', .7}. <br/>The sparsity is here the number of non-zero elements.
-%> @param 'backend',int (optional) the backend (the C++ implementation) chosen. Must be 2016 (the default) or 2020 (which should be quicker for certain configurations - e.g. factorizing a Hadamard matrix).
+%> @param 'backend',int (optional) the backend to use (the C++ implementation). Must be 2016 (the default) or 2020 (which should be faster for most of the factorizations).
 %> @param 'gpu', bool (optional) set to true to execute the algorithm using the GPU implementation. This option is only available when backend==2020.
 %>
 %> @note - The fully defined parameters (ParamsHierarchical instance) used/generated by the function are available in the return result (so one can consult what precisely mean the simplified parameterizations and possibly adjust the attributes to factorize again).

wrapper/matlab/+matfaust/+fact/palm4msa.m

@@ -4,7 +4,7 @@
 %>
 %> @param M the dense matrix to factorize.
 %> @param p the matfaust.factparams.ParamsPalm4MSA instance to define the algorithm parameters.
-%> @param 'backend',int (optional) the backend (the C++ implementation) chosen. Must be 2016 (the default) or 2020 (which should be quicker for certain configurations - e.g. factorizing a Hadamard matrix).
+%> @param 'backend',int (optional) the backend to use (the C++ implementation). Must be 2016 (the default) or 2020 (which should be faster for most of the factorizations).
 %> @param 'gpu', bool (optional) set to true to execute the algorithm using the GPU implementation. This option is only available when backend==2020.
 %>
 %> @retval F the Faust object result of the factorization.

wrapper/python/pyfaust/fact.py

@@ -477,6 +477,8 @@ def palm4msa(M, p, ret_lambda=False, backend=2016, on_gpu=False):
         M: the numpy array to factorize.
         p: the The pyfaust.factparams.ParamsPalm4MSA instance to define the algorithm parameters.
         ret_lambda: set to True to ask the function to return the scale factor (False by default).
+        backend: the C++ implementation to use (default to 2016, 2020 backend
+        should be faster for most of the factorizations).
         on_gpu: if True the GPU implementation is executed (this option applies only to 2020 backend).
 
     Returns:
@@ -677,8 +679,7 @@ def hierarchical(M, p, ret_lambda=False, ret_params=False, backend=2016,
             pyfaust.factparams.ParamsHierarchical and pyfaust.factparams.ParamsHierarchicalRectMat).
             <br/>The residuum has a sparsity of P*rho^(num_facts-1). <br/> By default, rho == .8 and P = 1.4. It's possible to set custom values with for example p == ( ['rectmat', j, k, s], {'rho':.4, 'P':.7 }). <br/>The sparsity is here the number of non-zero elements.
         backend: the C++ implementation to use (default to 2016, 2020 backend
-        should be faster for certain configurations - e.g. factorizing a
-        Hadamard matrix).
+        should be faster for most of the factorizations).
         on_gpu: if True the GPU implementation is executed (this option applies only to 2020 backend).
 
         ret_lambda: set to True to ask the function to return the scale factor (False by default).