Update Rd files and remove unused

DESCRIPTION

@@ -16,7 +16,7 @@ Version: 1.2.0
 Date: 2024-02-29
 Maintainer: Vissarion Fisikopoulos <[email protected]>
 Depends: Rcpp (>= 0.12.17)
-Imports: methods, stats
+Imports: methods, stats, Matrix
 LinkingTo: Rcpp, RcppEigen, BH
 Suggests: testthat
 Encoding: UTF-8

NAMESPACE

@@ -19,8 +19,6 @@ export(gen_simplex)
 export(gen_skinny_cube)
 export(geweke)
 export(inner_ball)
-export(loadSdpaFormatFile)
-export(ode_solve)
 export(pinvweibull_with_loc)
 export(psrf_multivariate)
 export(psrf_univariate)
@@ -30,10 +28,10 @@ export(rotate_polytope)
 export(round_polytope)
 export(sample_points)
 export(volume)
-export(writeSdpaFormatFile)
 export(write_sdpa_format_file)
 export(zonotope_approximation)
 exportClasses(Hpolytope)
+exportClasses(HpolytopeSparse)
 exportClasses(Spectrahedron)
 exportClasses(Vpolytope)
 exportClasses(VpolytopeIntersection)

NEWS.md

@@ -54,7 +54,7 @@
 # volesti 1.2.0
 
 - New functions: dinvweibull_with_loc, ess, estimtate_lipschitz_constant, gen_birkhoff, geweke
-ode_solve, pinvweibull_with_loc, psrf_multivariate, psrf_univariate, raftery
+pinvweibull_with_loc, psrf_multivariate, psrf_univariate, raftery
 
 - New features in sample_points function:
  a) new walks: i) Dikin walk, ii) Vaidya walk, iii) John walk,

R/HpolytopeSparseClass.R

@@ -1,26 +1,41 @@
-#' An R class to represent an H-polytope
+#' An R class to represent an H-polytope defined by a sparse matrix
 #'
-#' An H-polytope is a convex polytope defined by a set of linear inequalities or equivalently a \eqn{d}-dimensional H-polytope with \eqn{m} facets is defined by a \eqn{m\times d} matrix A and a \eqn{m}-dimensional vector b, s.t.: \eqn{Ax\leq b}.
+#' A sparse H-polytope is a convex polytope defined by a set of linear inequalities or equivalently a
+#' \eqn{d}-dimensional H-polytope with \eqn{m} facets is defined by a \eqn{m\times d} matrix A and
+#' a \eqn{m}-dimensional vector b, s.t.: \eqn{Ax\leq b}, where $A$ is sparse.
 #'
 #' \describe{
-#'    \item{A}{An \eqn{m\times d} numerical matrix.}
+#'    \item{Aineq}{An \eqn{m\times d} sparse numerical matrix for the inequalities.}
 #'
-#'    \item{b}{An \eqn{m}-dimensional vector b.}
+#'    \item{bineq}{An \eqn{m}-dimensional vector for the ineqaulities.}
 #'
-#'    \item{volume}{The volume of the polytope if it is known, \eqn{NaN} otherwise by default.}
+#'    \item{Aineq}{An \eqn{m'\times d} sparse numerical matrix for the equalities.}
 #'
-#'    \item{type}{A character with default value 'Hpolytope', to declare the representation of the polytope.}
+#'    \item{bineq}{An \eqn{m'}-dimensional vector for the eqaulities.}
+#'
+#'    \item{lb}{\eqn{d}-dimensional vector lb.}
+#'
+#'    \item{ub}{\eqn{d}-dimensional vector ub.}
+#'
+#'    \item{type}{A character with default value 'HpolytopeSparse', to declare the representation of the polytope.}
 #' }
 #'
 #' @examples
-#' A = matrix(c(-1,0,0,-1,1,1), ncol=2, nrow=3, byrow=TRUE)
-#' b = c(0,0,1)
-#' P = Hpolytope(A = A, b = b)
+#' library(Matrix)
+#' bineq=c(10,10,10,10,10)
+#' Aineq = matrix(c(1,0,-0.25,-1,2.5,1,0.4,-1,-0.9,0.5), nrow=5, ncol=2, byrow = TRUE)
+#' Aineq = as( Aineq, 'dgCMatrix' )
+#' beq=(0)
+#' Aeq = matrix(, nrow=0, ncol=2, byrow = TRUE)
+#' Aeq=as( Aeq, 'dgCMatrix' )
+#' lb=-100000*c(1,1);
+#' ub=100000*c(1,1);
+#' P <- HpolytopeSparse(Aineq = Aineq, bineq = bineq, Aeq = Aeq, beq = beq, lb = lb, ub = ub)
 #'
 #' @name HpolytopeSparse-class
 #' @rdname HpolytopeSparse-class
 #' @exportClass HpolytopeSparse
-library(Matrix)
+#' @importFrom "Matrix" "CsparseMatrix"
 HpolytopeSparse <- setClass (
   # Class name
   "HpolytopeSparse",
@@ -33,19 +48,17 @@ HpolytopeSparse <- setClass (
     beq = "numeric",
     lb = "numeric",
     ub = "numeric",
-    dimension = "numeric",
     type = "character"
     ),
 
   # Initializing slots
   prototype = list(
-    Aineq = as(0, "CsparseMatrix"),
-    bineq = as.numeric(NULL),
-    Aeq = as(0, "CsparseMatrix"),
-    beq = as.numeric(NULL),
-    lb = as.numeric(NULL),
-    ub = as.numeric(NULL),
-    dimension = as.numeric(NaN),
+    Aineq = Matrix::sparseMatrix(i = integer(0), j = integer(0), x = double(0), dims = c(0, 0)),
+    bineq = numeric(0),
+    Aeq = Matrix::sparseMatrix(i = integer(0), j = integer(0), x = double(0), dims = c(0, 0)),
+    beq = numeric(0),
+    lb = numeric(0),
+    ub = numeric(0),
     type = "HpolytopeSparse"
   )
 )

R/RcppExports.R

@@ -187,30 +187,6 @@ load_sdpa_format_file <- function(input_file = NULL) {
     .Call(`_volesti_load_sdpa_format_file`, input_file)
 }
 
-#' Solve an ODE of the form dx^n / dt^n = F(x, t)
-#'
-#' @param n The number of steps.
-#' @param step_size The step size.
-#' @param order The ODE order (default is n = 1)
-#' @param dimension The dimension of each derivative
-#' @param initial_time The initial time
-#' @param F The function oracle F(x, t) in the ODE.
-#' @param method The method to be used
-#' @param initial_conditions The initial conditions provided to the solver. Must be provided in a list with keys "x_1", ..., "x_n" and column vectors as values. The state "x_n" represents the (n-1)-th order derivative with respect to time
-#' @param domains A list of n H-polytopes with keys "P_1", "P_2", ..., "P_n" that correspond to each derivative's domain
-#'
-#' @return A list which contains elements "x_1", ..., "x_n" representing each derivative results. Each "x_i" corresponds to a d x n matrix where each column represents a certain timestep of the solver.
-#'
-#' @examples
-#' F <- function (x) (-x)
-#' initial_conditions <- list("x_1" = c(0), "x_2" = c(1))
-#' states <- ode_solve(dimension=1, n=1000, F=F, initial_time=0, step_size=0.01, order=2, method="leapfrog", initial_conditions=initial_conditions, domains = list())
-#'
-#' @export
-ode_solve <- function(n, step_size, order, dimension, initial_time, F, method, domains = NULL, initial_conditions = NULL) {
-    .Call(`_volesti_ode_solve`, n, step_size, order, dimension, initial_time, F, method, domains, initial_conditions)
-}
-
 #' An internal Rccp function as a polytope generator
 #'
 #' @param kind_gen An integer to declare the type of the polytope.
@@ -384,44 +360,6 @@ sample_points <- function(P, n, random_walk = NULL, distribution = NULL, seed =
     .Call(`_volesti_sample_points`, P, n, random_walk, distribution, seed)
 }
 
-#' Write a SDPA format file
-#'
-#' Outputs a spectrahedron (the matrices defining a linear matrix inequality) and a vector (the objective function)
-#' to a SDPA format file.
-#'
-#' @param spectrahedron A spectrahedron in n dimensions; must be an object of class Spectrahedron
-#' @param objectiveFunction A numerical vector of length n
-#' @param outputFile Name of the output file
-#'
-#' @examples
-#' \dontrun{
-#' A0 = matrix(c(-1,0,0,0,-2,1,0,1,-2), nrow=3, ncol=3, byrow = TRUE)
-#' A1 = matrix(c(-1,0,0,0,0,1,0,1,0), nrow=3, ncol=3, byrow = TRUE)
-#' A2 = matrix(c(0,0,-1,0,0,0,-1,0,0), nrow=3, ncol=3, byrow = TRUE)
-#' lmi = list(A0, A1, A2)
-#' S = Spectrahedron(matrices = lmi)
-#' objFunction = c(1,1)
-#' writeSdpaFormatFile(S, objFunction, "output.txt")
-#' }
-#' @export
-writeSdpaFormatFile <- function(spectrahedron = NULL, objectiveFunction = NULL, outputFile = NULL) {
-    invisible(.Call(`_volesti_writeSdpaFormatFile`, spectrahedron, objectiveFunction, outputFile))
-}
-
-#' Read a SDPA format file
-#'
-#' @param inputFile Name of the input file
-#'
-#' @return A list with two named items: an item "matrices" which is a list of the matrices and an vector "objFunction"
-#'
-#' @examples
-#' path = system.file('extdata', package = 'volesti')
-#' l = loadSdpaFormatFile(paste0(path,'/sdpa_n2m3.txt'))
-#' @export
-loadSdpaFormatFile <- function(inputFile = NULL) {
-    .Call(`_volesti_loadSdpaFormatFile`, inputFile)
-}
-
 #' The main function for volume approximation of a convex Polytope (H-polytope, V-polytope, zonotope or intersection of two V-polytopes). It returns a list with two elements: (a) the logarithm of the estimated volume and (b) the estimated volume
 #'
 #' For the volume approximation can be used three algorithms. Either CoolingBodies (CB) or SequenceOfBalls (SOB) or CoolingGaussian (CG). An H-polytope with \eqn{m} facets is described by a \eqn{m\times d} matrix \eqn{A} and a \eqn{m}-dimensional vector \eqn{b}, s.t.: \eqn{P=\{x\ |\  Ax\leq b\} }. A V-polytope is defined as the convex hull of \eqn{m} \eqn{d}-dimensional points which correspond to the vertices of P. A zonotope is desrcibed by the Minkowski sum of \eqn{m} \eqn{d}-dimensional segments.