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SOC-class

The SOC class.


Description

This class represents a second-order cone constraint, i.e. \|x\|_2 ≤q t.

Usage

SOC(t, X, axis = 2, id = NA_integer_)

## S4 method for signature 'SOC'
as.character(x)

## S4 method for signature 'SOC'
residual(object)

## S4 method for signature 'SOC'
get_data(object)

## S4 method for signature 'SOC'
format_constr(object, eq_constr, leq_constr, dims, solver)

## S4 method for signature 'SOC'
num_cones(object)

## S4 method for signature 'SOC'
size(object)

## S4 method for signature 'SOC'
cone_sizes(object)

## S4 method for signature 'SOC'
is_dcp(object)

## S4 method for signature 'SOC'
is_dgp(object)

## S4 method for signature 'SOC'
canonicalize(object)

Arguments

t

The scalar part of the second-order constraint.

X

A matrix whose rows/columns are each a cone.

axis

The dimension along which to slice: 1 indicates rows, and 2 indicates columns. The default is 2.

id

(Optional) A numeric value representing the constraint ID.

x, object

A SOC object.

eq_constr

A list of the equality constraints in the canonical problem.

leq_constr

A list of the inequality constraints in the canonical problem.

dims

A list with the dimensions of the conic constraints.

solver

A string representing the solver to be called.

Methods (by generic)

  • residual: The residual of the second-order constraint.

  • get_data: Information needed to reconstruct the object aside from the args.

  • format_constr: Format SOC constraints as inequalities for the solver.

  • num_cones: The number of elementwise cones.

  • size: The number of entries in the combined cones.

  • cone_sizes: The dimensions of the second-order cones.

  • is_dcp: An SOC constraint is DCP if each of its arguments is affine.

  • is_dgp: Is the constraint DGP?

  • canonicalize: The canonicalization of the constraint.

Slots

t

The scalar part of the second-order constraint.

X

A matrix whose rows/columns are each a cone.

axis

The dimension along which to slice: 1 indicates rows, and 2 indicates columns. The default is 2.


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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