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SCS-class

An interface for the SCS solver

Description

An interface for the SCS solver

Usage

SCS()

## S4 method for signature 'SCS'
mip_capable(solver)

## S4 method for signature 'SCS'
status_map(solver, status)

## S4 method for signature 'SCS'
name(x)

## S4 method for signature 'SCS'
import_solver(solver)

## S4 method for signature 'SCS'
reduction_format_constr(object, problem, constr, exp_cone_order)

## S4 method for signature 'SCS,Problem'
perform(object, problem)

## S4 method for signature 'SCS,list,list'
invert(object, solution, inverse_data)

## S4 method for signature 'SCS'
solve_via_data(
  object,
  data,
  warm_start,
  verbose,
  feastol,
  reltol,
  abstol,
  num_iter,
  solver_opts,
  solver_cache
)

Arguments

solver, object, x

A SCS object.
status

A status code returned by the solver.
problem

A Problem object.
constr

A Constraint to format.
exp_cone_order

A list indicating how the exponential cone arguments are ordered.
solution

The raw solution returned by the solver.
inverse_data

A list containing data necessary for the inversion.
data

Data generated via an apply call.
warm_start

A boolean of whether to warm start the solver.
verbose

A boolean of whether to enable solver verbosity.
feastol

The feasible tolerance on the primal and dual residual.
reltol

The relative tolerance on the duality gap.
abstol

The absolute tolerance on the duality gap.
num_iter

The maximum number of iterations.
solver_opts

A list of Solver specific options
solver_cache

Cache for the solver.

Methods (by generic)

  • mip_capable: Can the solver handle mixed-integer programs?

  • status_map: Converts status returned by SCS solver to its respective CVXPY status.

  • name: Returns the name of the solver

  • import_solver: Imports the solver

  • reduction_format_constr: Return a linear operator to multiply by PSD constraint coefficients.

  • perform: Returns a new problem and data for inverting the new solution

  • invert: Returns the solution to the original problem given the inverse_data.

  • solve_via_data: Solve a problem represented by data returned from apply.

CVXR

Disciplined Convex Optimization

v1.0-10
Apache License 2.0 | file LICENSE
Authors
Anqi Fu [aut, cre], Balasubramanian Narasimhan [aut], David W Kang [aut], Steven Diamond [aut], John Miller [aut], Stephen Boyd [ctb], Paul Kunsberg Rosenfield [ctb]
Initial release

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