6 files changed, 45 insertions(+), 27 deletions(-)

M ChangeLog
M DESCRIPTION
M NEWS
M man/compare_vectors.Rd
M man/neighbourfun.Rd
M vignettes/neighbours.Rnw

@@ 1,3 1,7 @@
+2022-08-16  Enrico Schumann  <es@enricoschumann.net>
+
+        * DESCRIPTION (Version): 0.1-0
+
 2022-08-10  Enrico Schumann  <es@enricoschumann.net>
 
         * R/neighbourfun.R (neighbourfun): fix "permute"


@@ 10,7 14,8 @@
 
 2021-09-01  Enrico Schumann  <es@enricoschumann.net>
 
-        * R/neighbourfun.R (ans): add type "permute"
+        * R/neighbourfun.R (neighbourfun): add type
+        "permute"
 
 2020-06-05  Enrico Schumann  <es@enricoschumann.net>
 

@@ 2,7 2,7 @@ Package: neighbours
 Type: Package
 Title: Neighbourhood Functions for Local-Search Algorithms
 Version: 0.1-0
-Date: 2022-08-15
+Date: 2022-08-16
 Maintainer: Enrico Schumann <es@enricoschumann.net>
 Authors@R: person(given = "Enrico", family = "Schumann",
                   role  = c("aut", "cre"),


@@ 18,7 18,7 @@ Description: Neighbourhood functions are key components of
   solution.  Supported are numeric and logical solutions.
   The algorithms were originally created for
   portfolio-optimisation applications, but can be used for
-  other models as well.  Several recipies for neighbour
+  other models as well.  Several recipes for neighbour
   computations are taken from "Numerical Methods and
   Optimization in Finance" by M. Gilli, D. Maringer and
   E. Schumann (2019, ISBN:978-0128150658).

@@ 1,4 1,4 @@
-v0.1-0  (2022-08-01)
+v0.1-0  (2022-08-16)
 
   o Initial release.  The package provides a function
     neighbourfun() that constructs neighbourhood


@@ 8,4 8,3 @@ v0.1-0  (2022-08-01)
     applications, but can be used for other models,
     such as variable selection.  The package also
     provides a helper function for comparing solutions.
-

@@ 50,7 50,7 @@ nb <- neighbourfun(type = "logical")
 
 compare_vectors(x, nb(x))
 ## 01010
-##  |   
+##  |
 ## 00010
 ## The vectors differ in  1  place.
 

@@ 50,12 50,13 @@ neighborfun (min = 0, max = 1, kmin = NULL, kmax = NULL,
     length-one logical vector, which means that all
     elements may be changed.
   }
-  \item{update}{either \code{logical} (the default is \code{FALSE}) or
-    character, specifying the type of
-    updating. Currently supported is \code{"Ax"}, in
-    which case the matrix \code{A} must be specified. See
-    examples.
-  }
+
+  \item{update}{either \code{logical} (the default
+    \code{FALSE}) or a string, specifying the type of
+    updating. Currently supported is \code{"Ax"}, in which
+    case the matrix \code{A} must be specified. See
+    Examples.}
+
   \item{A}{a numeric matrix}
   \item{type}{string: either \code{"numeric"},
     \code{"logical"} or \code{"permute"}


@@ 71,34 72,48 @@ neighborfun (min = 0, max = 1, kmin = NULL, kmax = NULL,
   and \code{...}, which can be used for local-search
   algorithms.
 
-  \code{neighborfun} is an alias for \code{neighbourfun}.
-
-
   Three types of solution vectors are supported:
   \describe{
 
-    \item{\code{numeric}}{a neighbour is created by adding or
-      subtracting typically small numbers to random elements of a solution}
-    \item{\code{logical}}{\code{\link{TRUE}},
-      \code{\link{FALSE}} values are switched}
-    \item{\code{permute}}{elements of \code{x} are exchanged. Works with
-      atomic and generic vectors (aka lists).}
-  }
+    \item{\code{numeric }}{a neighbour is created by adding
+      or subtracting typically small numbers to random
+      elements of a solution}%
+
+    \item{\code{logical} }{\code{\link{TRUE}} and
+      \code{\link{FALSE} } values are switched}%
+
+    \item{\code{permute}}{elements of \code{x} are
+      exchanged. Works with atomic and generic vectors (aka
+      lists).}
+  }%
+
+  \code{neighborfun} is an alias for \code{neighbourfun}.
 
 }
 \value{
 
   A function (closure) with arguments \code{x} and
-  \code{...}.
+  \code{\dots}.
+
+}
+\section{Note on algorithms}{
+
+  There are different strategies to implement constraints in
+  local-search algorithms, and ultimately only experiments
+  show which strategy works well for a given problem class.
+  The algorithms used by \code{\link{neighbourfun}} always
+  require a feasible initial solution, and then remain
+  within the space of feasible solutions. See Gilli et
+  al. (2019), Section 12.5, for a brief discussion.
 
 }
 \references{
 
   Gilli, M., Maringer, D. and Schumann, E. (2019) \emph{Numerical
-  Methods and Optimization in Finance}. 2nd edition. Elsevier.
+  Methods and Optimization in Finance}. 2nd edition. Elsevier.\cr
   \url{https://www.elsevier.com/books/numerical-methods-and-optimization-in-finance/gilli/978-0-12-815065-8}
 
-  Schumann, E. (2019) Financial Optimisation with R (\pkg{NMOF} Manual).
+  Schumann, E. (2019) Financial Optimisation with R (\pkg{NMOF} Manual).\cr
   \url{http://enricoschumann.net/NMOF.htm#NMOFmanual}
 
 }

@@ 50,8 50,7 @@ be used.  We start by attaching the package and setting
 a seed.
 <<package-seed>>=
 library("neighbours")
-set.seed(347343)
-# set.seed(34734)
+set.seed(3477)
 @
 
 \noindent In the examples that follow, we will use a