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Solve a cone program and return forward / adjoint derivative operators

Source: R/cone_program.R
solve_and_derivative.Rd

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.