% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cone_program.R
\name{solve_only}
\alias{solve_only}
\title{Solve a cone program (forward only)}
\usage{
solve_only(
  A,
  b,
  c,
  cone_dict,
  warm_start = NULL,
  solve_method = "Clarabel",
  P = NULL,
  ...
)
}
\arguments{
\item{A}{A sparse \code{dgCMatrix} constraint matrix.}

\item{b}{A numeric offset vector.}

\item{c}{A numeric objective coefficient vector.}

\item{cone_dict}{A named list with cone sizes (keys among
\code{"z"}, \code{"l"}, \code{"q"}, \code{"s"}, \code{"ep"}, \code{"ed"}).}

\item{warm_start}{Optional warm-start \code{list(x, y, s)}.}

\item{solve_method}{One of \code{"Clarabel"} (default) or \code{"SCS"}.}

\item{P}{Optional sparse \code{dgCMatrix} for QP objective.}

\item{...}{Additional control parameters forwarded to the solver.}
}
\value{
A list with elements \code{x}, \code{y}, \code{s}, \code{info}.
}
\description{
Mirrors \code{diffcp.cone_program.solve_only}. Solves
minimize  c^T x  (+ 0.5 x^T P x)
subject to  A x + s = b,   s in K
and returns the optimal \verb{(x, y, s)}. Unlike \code{solve_and_derivative}
this function does not build the derivative closures.
}
