From 0dcbc1eda07f84a5204c82f1c34a33a4c62d1483 Mon Sep 17 00:00:00 2001
From: Timothy <75321887+timothy-nunn@users.noreply.github.com>
Date: Tue, 15 Aug 2023 09:53:39 +0100
Subject: [PATCH] Remove QSP tolerance in favour of generic options dict (#7)

---
 src/pyvmcon/vmcon.py | 28 +++++++++++++++++++---------
 1 file changed, 19 insertions(+), 9 deletions(-)

diff --git a/src/pyvmcon/vmcon.py b/src/pyvmcon/vmcon.py
index c1d3728..d16b1f6 100644
--- a/src/pyvmcon/vmcon.py
+++ b/src/pyvmcon/vmcon.py
@@ -1,4 +1,4 @@
-from typing import Union, Optional
+from typing import Union, Optional, Dict, Any
 import logging
 import numpy as np
 import cvxpy as cp
@@ -22,7 +22,7 @@ def solve(
     *,
     max_iter: int = 10,
     epsilon: float = 1e-8,
-    qsp_tolerence: float = 1e-4,
+    qsp_options: Optional[Dict[str, Any]] = None,
     initial_B: Optional[np.ndarray] = None,
 ):
     """The main solving loop of the VMCON non-linear constrained optimiser.
@@ -47,6 +47,11 @@ def solve(
     epsilon : float
         The tolerance used to test if VMCON has converged
 
+    qsp_options : Optional[Dict[str, Any]]
+        Dictionary of keyword arguments that are passed to the
+        CVXPY `Probelem.solve` method. `None` will pass no
+        additional arguments to the solver.
+
     initial_B : ndarray
         Initial estimate of the Hessian matrix `B`. If `None`, `B` is the
         identity matrix of shape `(max(n,m), max(n,m))`.
@@ -115,7 +120,7 @@ def solve(
         # for our constraints
         try:
             delta, lamda_equality, lamda_inequality = solve_qsp(
-                problem, result, x, B, lbs, ubs, qsp_tolerence
+                problem, result, x, B, lbs, ubs, qsp_options or {}
             )
         except _QspSolveException as e:
             raise QSPSolverException(
@@ -188,7 +193,7 @@ def solve_qsp(
     B: np.ndarray,
     lbs: Optional[np.ndarray],
     ubs: Optional[np.ndarray],
-    tolerance: float,
+    options: Dict[str, Any],
 ):
     """Solves the quadratic programming problem detailed in equation 4 and 5
     of the VMCON paper.
@@ -216,10 +221,15 @@ def solve_qsp(
     ubs : ndarray
         The upper bounds of `x`.
 
-    tolerance : float
-        The relative tolerance of the QSP solver.
-        See https://www.cvxpy.org/tutorial/advanced/index.html#setting-solver-options
-        `eps_rel`.
+    options : Dict[str, Any]
+        Dictionary of keyword arguments that are passed to the
+        CVXPY `Probelem.solve` method.
+
+    Notes
+    -----
+    * By default, OSQP (https://osqp.org/) is the `solver` used in
+    the `solve` method however this can be changed by specifying a
+    different `solver` in the `options` dictionary.
     """
     delta = cp.Variable(x.shape)
     problem_statement = cp.Minimize(
@@ -244,7 +254,7 @@ def solve_qsp(
         constraints.append((result.deq @ delta) + result.eq == 0)
 
     qsp = cp.Problem(problem_statement, constraints or None)
-    qsp.solve(verbose=False, solver=cp.OSQP, eps_rel=tolerance)
+    qsp.solve(**{"solver": cp.OSQP, **options})
 
     if delta.value is None:
         raise _QspSolveException(f"QSP failed to solve: {qsp.status}")