Package 'diffcp'

Parse an SCS-style cone dictionary into an ordered list

Description

Mirrors diffcp.cones.parse_cone_dict.

Usage

parse_cone_dict(cone_dict)

Arguments

cone_dict

A named list with keys among "z", "l", "q", "s", "ep", "ed". Values are scalar dimensions (for z, l, ep, ed) or integer vectors of dimensions (for q, s).

Value

A list of list(name, size) pairs, in the canonical SCS cone order (CONES).

---

Projection onto a Cartesian product of cones

Description

Mirrors diffcp.cones.pi.

Usage

pi(x, cones, dual = FALSE)

Arguments

x	A numeric vector.
cones	A list of list(name, size) pairs, as produced by parse_cone_dict.
dual	If TRUE, project onto the dual cones.

Value

A numeric vector of the same length as x.

---

Solve a cone program and return forward / adjoint derivative operators

Description

Mirrors diffcp.cone_program.solve_and_derivative. Solves minimize c^T x s.t. A x + s = b, s in K (with optional QP ⁠0.5 x^T P x⁠ term) and returns the optimal ⁠(x, y, s)⁠ together with closures D (forward) and DT (adjoint) that map perturbations of ⁠(A, b, c, [P])⁠ to perturbations of ⁠(x, y, s)⁠ and vice versa.

Usage

solve_and_derivative(
  A,
  b,
  c,
  cone_dict,
  P = NULL,
  solve_method = "Clarabel",
  mode = "lsqr",
  warm_start = NULL,
  ...
)

Arguments

A	A sparse dgCMatrix constraint matrix.
b	A numeric offset vector.
c	A numeric objective coefficient vector.
cone_dict	A named list with cone sizes (keys among "z", "l", "q", "s", "ep", "ed").
P	Optional sparse dgCMatrix for QP objective.
solve_method	One of "Clarabel" (default) or "SCS".
mode	Differentiation mode: "lsqr" (default), "dense", or "lpgd".
warm_start	Optional warm-start list(x, y, s).
...	Additional control parameters forwarded to the solver.

Value

A list with elements x, y, s, info, D, DT. info is the solver-status block returned by the underlying solver (status, iter, solveTime, pobj, ...); see solve_only for the same shape.

---

Solve a cone program (forward only)

Description

Mirrors 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 ⁠(x, y, s)⁠. Unlike solve_and_derivative this function does not build the derivative closures.

Usage

solve_only(
  A,
  b,
  c,
  cone_dict,
  warm_start = NULL,
  solve_method = "Clarabel",
  P = NULL,
  ...
)

Arguments

A	A sparse dgCMatrix constraint matrix.
b	A numeric offset vector.
c	A numeric objective coefficient vector.
cone_dict	A named list with cone sizes (keys among "z", "l", "q", "s", "ep", "ed").
warm_start	Optional warm-start list(x, y, s).
solve_method	One of "Clarabel" (default) or "SCS".
P	Optional sparse dgCMatrix for QP objective.
...	Additional control parameters forwarded to the solver.

Value

A list with elements x, y, s, info.

---

Inverse of vec_symm

Description

Inverse of vec_symm

Usage

unvec_symm(x, dim)

Arguments

x	A numeric vector of length n*(n+1)/2.
dim	The matrix dimension n.

Value

The corresponding ⁠n x n⁠ symmetric matrix.

---

Upstream Python diffcp pin

Description

Returns the upstream pin metadata for this R port: which cvxgrp/diffcp version / commit the R sources track, the date of that commit, and diffcp's own CVXPY runtime constraint. The authoritative record lives in inst/UPSTREAM.dcf and is read at call time.

Usage

upstream_info()

Value

A named character vector with one element per DCF field (Upstream, Version, Commit, ShortCommit, Date, URL, CVXPY, Snapshot, Notes).

Examples

upstream_info()
upstream_info()[["Version"]]

---

Symmetric vectorization (SCS convention)

Description

Returns a vectorized representation of a symmetric matrix X, with off-diagonal entries scaled by sqrt(2) to make the SCS-style dot product ⁠<vec_symm(A), vec_symm(B)> = trace(A B)⁠ hold.

Usage

vec_symm(X)

Arguments

X	A symmetric matrix.

Details

Mirrors diffcp.cones.vec_symm (Python).

Value

A numeric vector of length n*(n+1)/2.

---