*** empty log message ***

README

@@ -4,6 +4,7 @@ This directory contains  the PSBLAS library, version 2.0.
 Release Candidate 1: For the time being, even if the docs say that we
 have both real and complex, we only have real data. When we have
 complex data it will be declared RC2 or V2.0. 
+The users' guide is not yet completed. 
 
 
 The version 1.0 of the library was described in:

docs/pdf/datastruct.tex

@@ -15,8 +15,7 @@ The \hypertarget{spdata}{{\tt psb\_spmat\_type}} data structure
 contains all information about local portion of the sparse matrix and   
 its storage mode. Many of this fields are set in fully-transparent
 mode by PSBLAS-TOOLS routines when inserting a new sparse matrix, user
-must set only fields which describe matrix storage mode (see
-\S~\ref{psb_spall}). \\
+must set only fields which describe matrix storage mode. \\
 Fields contained in Sparse matrix structures are:
 \begin{description}
 \item[{\bf aspk}] Contains values of the local distributed sparse
@@ -52,7 +51,6 @@ as in Coordinate Storage or Compressed Sparse Rows, should be greater
 than or equal to the maximum column  index actually present in the sparse matrix.
 Specified as: integer variable.
 \end{description}
-Values assumed by this fields are compatible with ref. 1 (see \S~\ref{chap:appendix}).\\
 FORTRAN95 interface for distributed sparse matrices containing double precision
 real entries is defined as in figure~\ref{fig:spmattype}.
 \begin{figure}[h!]

docs/pdf/intro.tex

@@ -67,9 +67,8 @@ library~\cite{scalapack}.  The layered structure of the PSBLAS library
 is shown in figure~\ref{fig:psblas} ; lower layers of the library
 indicate an encapsulation relationship with upper layers. The ongoing
 discussion focuses on the Fortran~95 layer immediately below the
-application layer; two examples of iterative solvers built through the
-PSBLAS routines, will be also given in Section~\ref{sec:itmethd}.  The
-serial parts of the computation on each process are executed through
+application layer.
+The serial parts of the computation on each process are executed through
 calls to the serial sparse BLAS subroutines. In a similar way, the
 inter-process message exchanges are implemented through the Basic
 Linear Algebra Communication Subroutines (BLACS) library~\cite{BLACS}
@@ -114,11 +113,11 @@ internally defined in the PSBLAS software package:
 \item all the subroutine names follow the rule \verb|psb_xxname| where
   \verb|xx| can be either:
   \begin{itemize}
-  \item \verb|ds|: the routine is related to dense data, 
+  \item \verb|ge|: the routine is related to dense data, 
   \item \verb|sp|: the routine is related to sparse data, 
   \item \verb|cd|: the routine is related to communication descriptor (see~\ref{sec:datastruct}).
   \end{itemize}
-  For example the \verb|psb_dsins|, \verb|psb_spins| and
+  For example the \verb|psb_geins|, \verb|psb_spins| and
   \verb|psb_cdins| perform the same action (see~\ref{sec:toolsrout}) on
   dense matrices, sparse matrices and communication descriptors
   respectively.

docs/pdf/precs.tex

@@ -3,30 +3,17 @@
 
 % \section{Preconditioners}
 \label{sec:psprecs}
-Preconditioning is somehow regarded as ``black magic''. This is due to
-the fact that theory doesn't provide a reliable support in the choice
-of a preconditioner. It is clear that the influence of a
-preconditioning technique on the convergence behavior of an iterative
-method mostly depends on the characteristics of the system matrix and
-of the method itself. Anyway it is not possible a priori to say that
-one preconditioner is algebrically better than another and this perfectly explains
-the importance of providing a wide range of preconditioners techniques
-so that the user can find by itself which one is more suitable for his
-problem. Moreover, there are some other issues to consider when
-choosing a preconditioner such as balancing the overhead of building
-the preconditioner with the reduction in the number of
-iterations. PSBLAS contains the implementation of many preconditioning
+PSBLAS contains the implementation of many preconditioning
 techniques some of which are very flexible thanks to the presence of
 many parameters that is possible to adjust to fit the user's needs:
 \begin{itemize}
 \item Diagonal Scaling
 \item Block Jacobi with ILU(0) factorization
 \item Additive Schwarz with the Restricted Additive Schwarz and
-  Additive Schwarz with Harmonic extensions (see
-  chapter~\ref{sec:pprec})
+  Additive Schwarz with Harmonic extensions;
 \item Two-Level Additive Schwarz; this is actually a family of
   preconditioners since there is the possibility to choose between
-  many variants as explained in chapter~\ref{sec:mlprec}
+  many variants.
 \end{itemize}
 
 

docs/pdf/toolsrout.tex

@@ -1,5 +1,5 @@
 \section{Data management and initialization routines}
-\label{sec:toolrout}
+\label{sec:toolsrout}
 %
 %%   psb_alloc %%
 %