QSS: Quadratic-Separable Solver

QSS solves problems of the form

$$\begin{equation} \begin{array}{ll} \text{minimize} & (1/2) x^T P x + q^T x + r + g(x) \\ \text{subject to} & Ax = b \end{array} \end{equation}$$

where $x \in \bf{R}^n$ is the decision variable being optimized over. The objective is defined by a positive definite matrix $P \in \bf{S}^n+$, a vector $q \in \bf{R}^n$, a scalar $r \in \bf{R}$, and a $g$ that is separable in the entries of $x$, i.e., $g$ can be written as $$g(x) = \sum{i=1}^n gi(xi).$$ The constraints are defined by a matrix $A \in \bf{R}^{m \times n}$ and a vector $b \in \bf{R}^m$.

To use QSS, the user must specify $P$, $q$, $r$, $A$, $b$, as well as the $g_i$ from a built-in collection of separable functions.

Installation

pip install qss

Usage

After installing qss, import it with python import qss This will expose the QSS class which is used to instantiate a solver object: python solver = qss.QSS(data) Use the solve() method when ready to solve: python results = solver.solve(eps_abs=1e-5, eps_rel=1e-5, alpha=1.4, rho=0.1, max_iter=np.inf, precond=True, warm_start=False, reg=True, use_iter_refinement=False, verbose=False, )

Parameters

• data: dictionary with the following keys:

  • 'P', 'q', 'r', 'A', 'b' specify the quadratic part of the objective and the linear constraint as in the problem formulation above. 'P' and 'A' should be scipy.sparse CSC matrices or QSS LinearOperators (see below), 'q' and 'b' should be numpy arrays, and 'r' should be a scalar. 'A' and 'b' can be excluded from data or set to None if the linear equality constraints are not needed.

  • 'g' is a list of separable function definitions. Each separable function is declared as a dictionary with the following keys:

    • 'g': string that corresponds to a valid separable function name (see below for a list of supported functions).
    • 'args': 'weight' (default 1), 'scale' (default 1), 'shift' (default 0) allow the 'g' function to be applied in a weighted manner to a shifted and scaled input. Some functions take additional arguments, see below.
    • 'range': tuple specifying the start index and end index that the function should be applied to.

    Note that the zero function will be applied to any indices that don't have another function specified for them.

• eps_abs: scalar specifying absolute tolerance.

• eps_abs: scalar specifying relative tolerance.

• alpha: scalar specifying overstep size.

• rho: scalar specifying ADMM step size.

• max_iter: maximum number of ADMM iterations to perform.

• precond: boolean specifying whether to perform matrix equilibration.

• warm_start: boolean specifying whether to warm start upon a repeat call of solve().

• reg: boolean specifying whether to regularize KKT matrix. May fail on certain problem instances if set to False.

• use_iter_refinement: boolean, only matters if reg is True. Helps mitigate some of the accuracy loss due to regularization.

• verbose: boolean specifying whether to print verbose output.

Returns

A list containing the following: - objective: the objective value attained by the solution found by qss. - solution: the solution vector.

Separable functions

The following convex separable functions are supported ( $\mathcal{I}$ is the $\{0, \infty\}$ indicator function): Function | Parameters | $gi(x)$ --- | --- | --- zero | | $0$ abs | | $|x|$ `ispos| | $\mathcal I(x \geq 0)$ isneg| | $\mathcal I(x \leq 0)$ isbound|lb: lower bound (default 0),ub: upper bound (default 1) | $\mathcal I(l \leq x \leq u)$ is_zero| | $\mathcal I(x = 0)$ pos| | $\max\\{x, 0\\}$ neg| | $\max\\{-x, 0\\}$ quantile|tau: scalar in $(0, 1)$ | $0.5 \|x\| + (\tau - 0.5) x$ huber|M`: positive scalar | $x^2 \text{ if } |x| \leq M, 2M|x| - M^2 \text{ else}$

The following nonconvex separable functions are supported: Function | Parameters | $gi(x)$ --- | --- | --- card | | $0$ for $x = 0$; $1$ for $x \neq 0$ `isint| | $\mathcal I(x \text{ is an integer})$ isfiniteset|S: Python list of scalars | $\mathcal I(x \in S)$ is_bool` | | $\mathcal I(x \in \{0,1\})$

The t (weight), a (scale), b (shift) parameters are used to shift and scale the above as follows: t * g(ax - b).

Example

Applying the Huber function to a shifted version of the first 100 entries: python [{"g": "huber", "args": {"M": 2, "shift": -5}, "range": (0, 100)}]

Abstract linear operators

QSS comes with built-in support for abstract linear operators via the qss.linearoperator.LinearOperator class (hereafter referred to simply as LinearOperator).

The easiest way to build a LinearOperator is via its constructor. The argument to the constructor should be a list of lists representing a block matrix, in which each block is one of the following: - SciPy sparse matrix or - scipy.sparse.linalg.LinearOperator or - qss.linearoperator.LinearOperator or - None.

As an example, a constraint matrix A could be built as follows: ```python from qss.linearoperator import LinearOperator

A = LinearOperator([ [None, F, -I], [I, I, None] ]) `` WhereFis ascipy.sparse.linalg.LinearOperatorthat implements the Fourier transform andI` is a SciPy sparse identity matrix.

There are several helper functions available to facilitate the creation of LinearOperators, all accessible through qss.linearoperator: - block_diag(D): Returns a block diagonal LinearOperator from D, a list of linear operators (SciPy sparse matrix, scipy.sparse.linalg.LinearOperator, or qss.linearoperator.LinearOperator). - hstack(D): Horizontally concatenates list of linear operators D into a single LinearOperator. - vstack(D): Vertically concatenates a list of linear operators D into a single LinearOperator.

Left and right matrix multiplication between a LinearOperator and a NumPy array is supported. Multiplication between LinearOperators is currently not supported. Matrix-vector multiplication between a LinearOperator F and a NumPy array v can be achieved with F.matvec(v) or F @ v. Multiplication with the adjoint of F can be achieved with F.rmatvec(v) or v @ F.

Note that solve times may be slower when LinearOperators are involved. If either P or A is a LinearOperator, the linear KKT system central to the QSS algorithm is solved indirectly, as opposed to directly via a factorization.

Example

Nonnegative least squares is a problem of the form

$$\begin{equation} \begin{array}{ll} \text{minimize} & (1/2) \Vert Gx - h \Vert_2^2 \\ \text{subject to} & x \geq 0. \end{array} \end{equation} $$

qss can be used to solve this problem as follows: ```python import numpy as np import scipy as sp import qss

p = 100 n = 500 G = sp.sparse.random(n, p, density=0.2, format="csc") h = np.random.rand(n)

data = {} data["P"] = G.T @ G data["q"] = -h.T @ G data["r"] = 0.5 * h.T @ h data["g"] = [{"g": "is_pos", "range": (0, p)}] # Enforce x >= 0

solver = qss.QSS(data) objective, x = solver.solve() print(objective) ```

Development

To create a virtual environment, run python3 -m venv env Activate it with source env/bin/activate Clone the qss repository, cd into it, and install qss in development mode: pip install -e ./ -r requirements.txt Finally, test to make sure the installation worked: pytest tests/