title: "Hands-on experience with a constrained least squares problem" categories: Tech updated: 2021-08-05 comments: true
讲调包. 主问题是个凸二次规划, 找包调的时候顺便调研了 SciPy 处理一般带约束优化问题的不同算法的适用场景 (官方文档里并没有写得很明白).
An optimization problem with a quadratic objective function and linear constraints is called a quadratic program (QP). Consider the following (convex) QP,
$$ \begin{align} \min_\beta & \quad \Vert Y - X\beta \Vert_2^2, \ \text{subject to} & \quad \boldsymbol{1}_p'\beta = 1, \ & \quad \beta \succeq 0, \end{align} $$
with
The notation $\boldsymbol{1}_p$ stands for the $p\times 1$ vector $(1, \dots, 1)'$. The objection is equivalent to minimizing $\beta' X'X \beta - 2Y'X\beta$.
Here are two candidate solutions for Python currently, a SciPy solution and a CVXOPT solution. On top of these, there are packages like CVXPY for Python and oj! Algorithms for Java (QuadraticSolver).
oj! Algorithms – ojAlgo – is Open Source Java code to do mathematics, linear algebra and optimisation. It's what you need for data science, machine learning and scientific computing.
Optimisation (mathematical programming) tools including LP, QP and MIP solvers – again this is pure Java with zero dependencies.
Side note: The vector $\beta$ can be viewed as a probability distribution over $p$ column vectors of $X$, and thus $X\beta$ is simply the expectation of the underlying random vector. This fact is not leveraged in the following post.
SciPy implements three methods to solve general constrained minimization problems: trust-region constrained algorithm (trust-constr), sequential least squares programming (SLSQP) algorithm and constrained optimization by linear approximation (COBYLA) algorithm; see here for the problem formulation, and here for the documentation of the minimize function.
Use COBYLA if there are non-smooth functions
COBYLA is an non-gradient based method based on linear approximations to the objective function and each constraint. The algorithm operates by evaluating the objective function and the constrains at the vertices of a trust region. See here for a brief introduction for COBYLA and here for trust region methods..
In general, the convergence of COBYLA is slower than that of gradient-based algorithms, i.e. more function evaluations are required to find the optimum. However, one of the salient features of COBYLA is its stability and the low number of parameters to be tuned for performing optimization.
Use SLSQP for moderately large problems
Kraft (1988) claims that sequential quadratic programming (SQP) is known as to be the most efficient computation method to solve the general nonlinear programming problem with continuously differentiable objective function and constraints.
The size of the problem should only be moderately large with $m \le p \le 200$, where $m$ (=1 in our case) is the number of equality and inequality constraints (with bounds excluded), and $p$ is the dimension of the variable to be optimized. Wendorff et al. (2016) reported in their case study that SLSQP is not able to be run in parallel making a problem with large number of design variables intractable.
The idea of SQP is somewhat like Newton's method. SQP methods solve a sequence of optimization subproblems, each of which optimizes a quadratic model of the objective subject to a linearization of the constraints. According to Chapter 18 of Nocedal's numerical optimization book, SQP is also appropriate for large problems. Maybe the statement above only applies to Kraft's implementation (I don't know).
Use trust-constr method for large-scale problems
It is the most versatile constrained minimization algorithm implemented in SciPy and the most appropriate for large-scale problems.
For equality constrained problems it is an implementation of Byrd-Omojokun Trust-Region SQP method. When inequality constraints are imposed as well, it swiches to the trust-region interior point method described in An interior point algorithm for large-scale nonlinear programming (Byrd et al., 1999).
Byrd et al. (1999) assume in the paper that first and second derivatives of the objective function and constraints are available, but their strategy can be extended to make use of quasi-Newton approximations. Their algorithm incorporates within the interior point method two powerful tools for solving nonlinear problems: sequential quadratic programming (SQP) and trust region techniques.
References
Though lesser-known, it is a dedicated package for convex optimization, and can solve a more tailored problem suitable for our case.
See here for the description of the function to solve QPs. Below is the notation mapping table, where $I_p$ is the $p \times p$ identity matrix.
| cvxopt | original |
|---|---|
| $x$ | $\beta$ |
| $P$ | $2X'X$ |
| $q$ | $-2X'Y$ |
| $G$ | $-I_p$ |
| $h$ | $\boldsymbol{0}_p$ |
| $A$ | $\boldsymbol{1}_p'$ |
| $b$ | 1 |
Outputs
The function cvxopt.solvers.qp returns a dictionary with keys for some properties about the solution among which the 'status', 'x', and 'primal objective' are probably the most important. Read the docstring for more detail.
The 'status' field has values 'optimal' or 'unknown'.
There is a report The CVXOPT linear and quadratic cone program solvers (pdf) listed in its technical documentation. According to p. 11 of the report, QPs are solved using a path-following algorithm which is a kind of interior-point algorithms.
Based on the discussion above, SLSQP is used for SciPy.
import time
import cvxopt
import numpy as np
import tqdm
from scipy import optimize
def random_matrix(size):
return cvxopt.matrix(np.random.random_sample(size))
# used in scipy
def objection(beta, X, Y):
"""
Parameters
----------
beta : array (p,)
X : array (n, p)
Y : array (n,)
Returns
-------
scalar
"""
bias = Y - X.dot(beta)
return bias.dot(bias)
def jac(beta, X, Y):
"""
Parameters
----------
beta : array (p,)
X : array (n, p)
Y : array (n,)
Returns
-------
array (p,)
"""
return 2 * (X.T.dot(X).dot(beta) - X.T.dot(Y))
Run the test
np.random.seed(0xC7)
cvxopt.solvers.options['show_progress'] = False
count = 0
time_cvx = 0
time_sci = 0
count_cvx_fail = 0
count_sci_fail = 0
loss_relative_diff = 0
times = 100
for _ in tqdm.tqdm(range(times)):
# cvxopt
X = random_matrix((1000, 100))
n, p = X.size # should be X.shape if X is an numpy array
Y = random_matrix((n, 1))
# cvxopt notation
P = 2 * X.T * X # * means matrix multiplication in CVXOPT
q = -2 * X.T * Y
G = - cvxopt.matrix(np.eye(p))
h = cvxopt.matrix(0.0, size=(p, 1))
A = cvxopt.matrix(1.0, size=(1, p))
b = cvxopt.matrix(1.0)
start_time = time.time()
res_cvx = cvxopt.solvers.qp(P, q, G, h, A, b)
time_cvx += time.time() - start_time
if res_cvx['status'] != 'optimal':
count_cvx_fail += 1
print('CVXOPT failed')
loss_cvx = res_cvx['primal objective'] + (Y.T * Y)[0]
# scipy
X = np.array(X)
Y = np.array(Y).ravel() # shape (n,)
eq_cons = {'type': 'eq',
'fun': lambda beta: np.ones(beta.shape[0]).dot(beta) - 1,
'jac': lambda beta: np.ones(beta.shape[0])}
start_time = time.time()
res_sci = optimize.minimize(objection,
x0=(1 / p) * np.ones(p),
args=(X, Y),
method='SLSQP',
jac=jac,
bounds=optimize.Bounds(lb=0, ub=np.inf),
constraints=eq_cons)
time_sci += time.time() - start_time
if not res_sci['success']:
count_sci_fail += 1
print('SciPy failed')
loss_sci = res_sci['fun']
# print(loss_cvx, loss_sci)
print(f'In a test that randomly generates {n} x {p} matrix X and other vectors for {times} times')
print(f'cvxopt.solvers.qp cost {time_cvx:.2f}s, failed {count_cvx_fail} times')
print(f'scipy.optimize.minimize cost {time_sci:.2f}s, failed {count_sci_fail} times')
Note that we only used SLSQP method in SciPy, while trust-constr is said to be the most appropriate for large-scale problems.
It seems that
In a test that randomly generates 1000 x 100 matrices Xs and other vectors for 100 times
cvxopt.solvers.qp cost 1.06s, failed 0 times
scipy.optimize.minimize cost 25.03s, failed 0 times
60%|██████ | 18/30 [01:29<00:57, 4.76s/it]SciPy failed
In a test that randomly generates 1000 x 300 matrices Xs and other vectors for 30 times
cvxopt.solvers.qp cost 1.02s, failed 0 times
scipy.optimize.minimize cost 145.52s, failed 1 times
In a test that randomly generates 22 x 5 matrices Xs and other vectors for 5000 times
cvxopt.solvers.qp cost 9.48s, failed 0 times
scipy.optimize.minimize cost 5.80s, failed 0 times
In a test that randomly generates 10000 x 1000 matrix X and other vectors for 5 times
cvxopt.solvers.qp cost 3.13s, failed 0 times
scipy.optimize.minimize cost 1316.03s, failed 5 times