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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ecos.R
\name{ECOS_csolve}
\alias{ECOS_csolve}
\title{Solve a conic optimization problem}
\usage{
ECOS_csolve(
c = numeric(0),
G = NULL,
h = numeric(0),
dims = list(l = integer(0), q = NULL, e = integer(0)),
A = NULL,
b = numeric(0),
bool_vars = integer(0),
int_vars = integer(0),
control = ecos.control()
)
}
\arguments{
\item{c}{the coefficients of the objective function; the length of
this determines the number of variables \eqn{n} in the problem.}
\item{G}{the inequality constraint matrix in one of three forms: a
plain matrix, simple triplet matrix, or compressed column
format, e.g. \link[Matrix]{dgCMatrix-class}. Can also be
\code{NULL}}
\item{h}{the right hand side of the inequality constraint. Can be
empty numeric vector.}
\item{dims}{is a list of three named elements: \code{dims['l']} an
integer specifying the dimension of positive orthant cone,
\code{dims['q']} an integer vector specifying dimensions of
second-order cones, \code{dims['e']} an integer specifying the
number of exponential cones}
\item{A}{the optional equality constraint matrix in one of three
forms: a plain matrix, simple triplet matrix, or compressed
column format, e.g. \link[Matrix]{dgCMatrix-class}. Can be
\code{NULL}}
\item{b}{the right hand side of the equality constraint, must be
specified if \eqn{A} is. Can be empty numeric vector.}
\item{bool_vars}{the indices of the variables, 1 through \eqn{n},
that are boolean; that is, they are either present or absent in
the solution}
\item{int_vars}{the indices of the variables, 1 through \eqn{n},
that are integers}
\item{control}{is a named list that controls various optimization
parameters; see \link[ECOSolveR]{ecos.control}.}
}
\value{
a list of 8 named items
\describe{
\item{x}{primal variables}
\item{y}{dual variables for equality constraints}
\item{s}{slacks for \eqn{Gx + s <= h}, \eqn{s \in K}}
\item{z}{dual variables for inequality constraints \eqn{s \in K}}
\item{infostring}{gives information about the status of solution}
\item{retcodes}{a named integer vector containing four elements
\describe{
\item{exitflag}{0=\code{ECOS_OPTIMAL}, 1=\code{ECOS_PINF},
2=\code{ECOS_DINF}, 10=\code{ECOS_INACC_OFFSET}, -1=\code{ECOS_MAXIT},
-2=\code{ECOS_NUMERICS}, -3=\code{ECOS_OUTCONE}, -4=\code{ECOS_SIGINT},
-7=\code{ECOS_FATAL}. See \link[ECOSolveR]{ECOS_exitcodes}}.
\item{iter}{the number of iterations used}
\item{mi_iter}{the number of iterations for mixed integer problems}
\item{numerr}{a non-zero number if a numeric error occurred}
}
}
\item{summary}{a named numeric vector containing
\describe{
\item{pcost}{value of primal objective}
\item{dcost}{value of dual objective}
\item{pres}{primal residual on inequalities and equalities}
\item{dres}{dual residual}
\item{pinf}{primal infeasibility measure}
\item{dinf}{dual infeasibility measure}
\item{pinfres}{primal infeasibility residual}
\item{dinfres}{dual infeasibility residual}
\item{gap}{duality gap}
\item{relgap}{relative duality gap}
\item{r0}{Unknown at the moment to this R package maintainer.}
}
}
\item{timing}{a named numeric vector of timing information consisting of
\describe{
\item{runtime}{the total runtime in ecos}
\item{tsetup}{the time for setup of the problem}
\item{tsolve}{the time to solve the problem}
}
}
}
}
\description{
The function \code{ECOS_csolve} is a wrapper around the ecos
\code{csolve} C function. Conic constraints are specified using the
\eqn{G} and \eqn{h} parameters and can be \code{NULL} and zero
length vector respectively indicating an absence of conic
constraints. Similarly, equality constraints are specified via
\eqn{A} and \eqn{b} parameters with \code{NULL} and empty vector
values representing a lack of such constraints. At most one of the
pair \eqn{(G , h)} or \eqn{(A, b)} is allowed to be absent.
}
\section{Details}{
A call to this function will solve the problem:
minimize \eqn{c^Tx}, subject to \eqn{Ax = b}, and \eqn{h - G*x \in K}.
Variables can be constrained to be boolean (1 or 0) or integers. This is indicated
by specifying parameters \code{bool_vars} and/or \code{int_vars} respectively. If so
indicated, the solutions will be found using a branch and bound algorithm.
}
\examples{
## githubIssue98
cat("Basic matrix interface\n")
Gmat <- matrix(c(0.416757847405471, 2.13619609566845, 1.79343558519486, 0, 0,
0, 0, -1, 0, 0, 0, 0.056266827226329, -1.64027080840499, 0.841747365656204,
0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0.416757847405471, 2.13619609566845,
1.79343558519486, 0, 0, 0, -1, 0, 0, 0, 0, 0.056266827226329, -1.64027080840499,
0.841747365656204, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0), ncol = 5L)
c <- as.numeric(c(0, 0, 0, 0, 1))
h <- as.numeric(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))
dims <- list(l = 6L, q = 5L, e = 0L)
ECOS_csolve(c = c, G = Gmat, h = h,
dims = dims,
A = NULL, b = numeric(0))
cat("Simple Triplet Matrix interface, if you have package slam\n")
if (requireNamespace("slam")) {
ECOS_csolve(c = c, G = slam::as.simple_triplet_matrix(Gmat), h = h,
dims = dims,
A = NULL, b = numeric(0))
}
if (requireNamespace("Matrix")) {
ECOS_csolve(c = c, G = Matrix::Matrix(Gmat), h = h,
dims = dims,
A = NULL, b = numeric(0))
}
## Larger problems using saved data can be found in the test suite.
## Here is one
if (requireNamespace("Matrix")) {
MPC01 <- readRDS(system.file("testdata", "MPC01_1.RDS", package = "ECOSolveR"))
G <- Matrix::sparseMatrix(x = MPC01$Gpr, i = MPC01$Gir, p = MPC01$Gjc,
dims = c(MPC01$m, MPC01$n), index1 = FALSE)
h <- MPC01$h
dims <- lapply(list(l = MPC01$l, q=MPC01$q, e=MPC01$e), as.integer)
retval <- ECOS_csolve(c = MPC01$c, G=G, h = h, dims = dims, A = NULL, b = NULL,
control = ecos.control(verbose=1L))
retval$retcodes
retval$infostring
retval$summary
}
}
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