I would like students to solve a quadratic program in an assignment without them having to install extra software like cvxopt etc. Is there a python implementation available that only depends on NumPy/SciPy?
我希望学生在作业中解决二次方程,而不必安装像cvxopt等额外的软件。是否有可用的python实现只依赖于NumPy / SciPy?
4 个解决方案
#1
5
I ran across a good solution and wanted to get it out there. There is a python implementation of LOQO in the ELEFANT machine learning toolkit out of NICTA (http://elefant.forge.nicta.com.au as of this posting). Have a look at optimization.intpointsolver. This was coded by Alex Smola, and I've used a C-version of the same code with great success.
我遇到了一个很好的解决方案,想要把它拿出来。在NICTA的ELEFANT机器学习工具包中有一个关于LOQO的python实现(http://elefant.forge.nicta.com.au截至本帖子)。看看optimization.intpointsolver。这是由Alex Smola编写的,我使用相同代码的C版本取得了巨大成功。
#2
33
I'm not very familiar with quadratic programming, but I think you can solve this sort of problem just using scipy.optimize's constrained minimization algorithms. Here's an example:
我不太熟悉二次规划,但我认为你可以使用scipy.optimize的约束最小化算法来解决这类问题。这是一个例子:
import numpy as np
from scipy import optimize
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D
# minimize
# F = x[1]^2 + 4x[2]^2 -32x[2] + 64
# subject to:
# x[1] + x[2] <= 7
# -x[1] + 2x[2] <= 4
# x[1] >= 0
# x[2] >= 0
# x[2] <= 4
# in matrix notation:
# F = (1/2)*x.T*H*x + c*x + c0
# subject to:
# Ax <= b
# where:
# H = [[2, 0],
# [0, 8]]
# c = [0, -32]
# c0 = 64
# A = [[ 1, 1],
# [-1, 2],
# [-1, 0],
# [0, -1],
# [0, 1]]
# b = [7,4,0,0,4]
H = np.array([[2., 0.],
[0., 8.]])
c = np.array([0, -32])
c0 = 64
A = np.array([[ 1., 1.],
[-1., 2.],
[-1., 0.],
[0., -1.],
[0., 1.]])
b = np.array([7., 4., 0., 0., 4.])
x0 = np.random.randn(2)
def loss(x, sign=1.):
return sign * (0.5 * np.dot(x.T, np.dot(H, x))+ np.dot(c, x) + c0)
def jac(x, sign=1.):
return sign * (np.dot(x.T, H) + c)
cons = {'type':'ineq',
'fun':lambda x: b - np.dot(A,x),
'jac':lambda x: -A}
opt = {'disp':False}
def solve():
res_cons = optimize.minimize(loss, x0, jac=jac,constraints=cons,
method='SLSQP', options=opt)
res_uncons = optimize.minimize(loss, x0, jac=jac, method='SLSQP',
options=opt)
print '\nConstrained:'
print res_cons
print '\nUnconstrained:'
print res_uncons
x1, x2 = res_cons['x']
f = res_cons['fun']
x1_unc, x2_unc = res_uncons['x']
f_unc = res_uncons['fun']
# plotting
xgrid = np.mgrid[-2:4:0.1, 1.5:5.5:0.1]
xvec = xgrid.reshape(2, -1).T
F = np.vstack([loss(xi) for xi in xvec]).reshape(xgrid.shape[1:])
ax = plt.axes(projection='3d')
ax.hold(True)
ax.plot_surface(xgrid[0], xgrid[1], F, rstride=1, cstride=1,
cmap=plt.cm.jet, shade=True, alpha=0.9, linewidth=0)
ax.plot3D([x1], [x2], [f], 'og', mec='w', label='Constrained minimum')
ax.plot3D([x1_unc], [x2_unc], [f_unc], 'oy', mec='w',
label='Unconstrained minimum')
ax.legend(fancybox=True, numpoints=1)
ax.set_xlabel('x1')
ax.set_ylabel('x2')
ax.set_zlabel('F')
Output:
Constrained:
status: 0
success: True
njev: 4
nfev: 4
fun: 7.9999999999997584
x: array([ 2., 3.])
message: 'Optimization terminated successfully.'
jac: array([ 4., -8., 0.])
nit: 4
Unconstrained:
status: 0
success: True
njev: 3
nfev: 5
fun: 0.0
x: array([ -2.66453526e-15, 4.00000000e+00])
message: 'Optimization terminated successfully.'
jac: array([ -5.32907052e-15, -3.55271368e-15, 0.00000000e+00])
nit: 3
#3
9
This might be a late answer, but I found CVXOPT - http://cvxopt.org/ - as the commonly used free python library for Quadratic Programming. However, it is not easy to install, as it requires the installation of other dependencies.
这可能是一个迟到的答案,但我发现CVXOPT - http://cvxopt.org/ - 作为二次规划常用的免费python库。但是,安装起来并不容易,因为它需要安装其他依赖项。
#4
3
mystic provides a pure python implementation of nonlinear/non-convex optimization algorithms with advanced constraints functionality that typically is only found in QP solvers. mystic actually provides more robust constraints than most QP solvers. However, if you are looking for optimization algorithmic speed, then the following is not for you. mystic is not slow, but it's pure python as opposed to python bindings to C. If you are looking for flexibility and QP constraints functionality in a nonlinear solver, then you might be interested.
mystic提供了非线性/非凸优化算法的纯python实现,具有高级约束功能,通常只能在QP求解器中找到。 mystic实际上提供了比大多数QP求解器更强大的约束。但是,如果您正在寻找优化算法速度,那么以下内容不适合您。 mystic并不慢,但它是纯python而不是python绑定到C.如果你在非线性求解器中寻找灵活性和QP约束功能,那么你可能会感兴趣。
"""
Maximize: f = 2*x[0]*x[1] + 2*x[0] - x[0]**2 - 2*x[1]**2
Subject to: -2*x[0] + 2*x[1] <= -2
2*x[0] - 4*x[1] <= 0
x[0]**3 -x[1] == 0
where: 0 <= x[0] <= inf
1 <= x[1] <= inf
"""
import numpy as np
import mystic.symbolic as ms
import mystic.solvers as my
import mystic.math as mm
# generate constraints and penalty for a nonlinear system of equations
ieqn = '''
-2*x0 + 2*x1 <= -2
2*x0 - 4*x1 <= 0'''
eqn = '''
x0**3 - x1 == 0'''
cons = ms.generate_constraint(ms.generate_solvers(ms.simplify(eqn,target='x1')))
pens = ms.generate_penalty(ms.generate_conditions(ieqn), k=1e3)
bounds = [(0., None), (1., None)]
# get the objective
def objective(x, sign=1):
x = np.asarray(x)
return sign * (2*x[0]*x[1] + 2*x[0] - x[0]**2 - 2*x[1]**2)
# solve
x0 = np.random.rand(2)
sol = my.fmin_powell(objective, x0, constraint=cons, penalty=pens, disp=True,
bounds=bounds, gtol=3, ftol=1e-6, full_output=True,
args=(-1,))
print 'x* = %s; f(x*) = %s' % (sol[0], -sol[1])
Things to note is that mystic can generically apply LP, QP, and higher order equality and inequality constraints to any given optimizer, not just a special QP solver. Secondly, mystic can digest symbolic math, so the ease of defining/entering the constraints is a bit nicer than working with the matrices and derivatives of functions. mystic depends on numpy, and will use scipy if it is installed (however, scipy is not required). mystic utilizes sympy to handle symbolic constraints, but it's also not required for optimization in general.
值得注意的是,神秘主义者可以将LP,QP和更高阶等式和不等式约束应用于任何给定的优化器,而不仅仅是一个特殊的QP求解器。其次,神秘主义者可以消化符号数学,因此定义/输入约束的容易性比使用函数的矩阵和导数更好。神秘主义取决于numpy,如果安装了scipy将使用scipy(但是,不需要scipy)。 mystic利用sympy处理符号约束,但一般来说也不需要优化。
Output:
Optimization terminated successfully.
Current function value: -2.000000
Iterations: 3
Function evaluations: 103
x* = [ 2. 1.]; f(x*) = 2.0
Get mystic here: https://github.com/uqfoundation
在这里获取神秘主义:https://github.com/uqfoundation
#1
5
I ran across a good solution and wanted to get it out there. There is a python implementation of LOQO in the ELEFANT machine learning toolkit out of NICTA (http://elefant.forge.nicta.com.au as of this posting). Have a look at optimization.intpointsolver. This was coded by Alex Smola, and I've used a C-version of the same code with great success.
我遇到了一个很好的解决方案,想要把它拿出来。在NICTA的ELEFANT机器学习工具包中有一个关于LOQO的python实现(http://elefant.forge.nicta.com.au截至本帖子)。看看optimization.intpointsolver。这是由Alex Smola编写的,我使用相同代码的C版本取得了巨大成功。
#2
33
I'm not very familiar with quadratic programming, but I think you can solve this sort of problem just using scipy.optimize's constrained minimization algorithms. Here's an example:
我不太熟悉二次规划,但我认为你可以使用scipy.optimize的约束最小化算法来解决这类问题。这是一个例子:
import numpy as np
from scipy import optimize
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D
# minimize
# F = x[1]^2 + 4x[2]^2 -32x[2] + 64
# subject to:
# x[1] + x[2] <= 7
# -x[1] + 2x[2] <= 4
# x[1] >= 0
# x[2] >= 0
# x[2] <= 4
# in matrix notation:
# F = (1/2)*x.T*H*x + c*x + c0
# subject to:
# Ax <= b
# where:
# H = [[2, 0],
# [0, 8]]
# c = [0, -32]
# c0 = 64
# A = [[ 1, 1],
# [-1, 2],
# [-1, 0],
# [0, -1],
# [0, 1]]
# b = [7,4,0,0,4]
H = np.array([[2., 0.],
[0., 8.]])
c = np.array([0, -32])
c0 = 64
A = np.array([[ 1., 1.],
[-1., 2.],
[-1., 0.],
[0., -1.],
[0., 1.]])
b = np.array([7., 4., 0., 0., 4.])
x0 = np.random.randn(2)
def loss(x, sign=1.):
return sign * (0.5 * np.dot(x.T, np.dot(H, x))+ np.dot(c, x) + c0)
def jac(x, sign=1.):
return sign * (np.dot(x.T, H) + c)
cons = {'type':'ineq',
'fun':lambda x: b - np.dot(A,x),
'jac':lambda x: -A}
opt = {'disp':False}
def solve():
res_cons = optimize.minimize(loss, x0, jac=jac,constraints=cons,
method='SLSQP', options=opt)
res_uncons = optimize.minimize(loss, x0, jac=jac, method='SLSQP',
options=opt)
print '\nConstrained:'
print res_cons
print '\nUnconstrained:'
print res_uncons
x1, x2 = res_cons['x']
f = res_cons['fun']
x1_unc, x2_unc = res_uncons['x']
f_unc = res_uncons['fun']
# plotting
xgrid = np.mgrid[-2:4:0.1, 1.5:5.5:0.1]
xvec = xgrid.reshape(2, -1).T
F = np.vstack([loss(xi) for xi in xvec]).reshape(xgrid.shape[1:])
ax = plt.axes(projection='3d')
ax.hold(True)
ax.plot_surface(xgrid[0], xgrid[1], F, rstride=1, cstride=1,
cmap=plt.cm.jet, shade=True, alpha=0.9, linewidth=0)
ax.plot3D([x1], [x2], [f], 'og', mec='w', label='Constrained minimum')
ax.plot3D([x1_unc], [x2_unc], [f_unc], 'oy', mec='w',
label='Unconstrained minimum')
ax.legend(fancybox=True, numpoints=1)
ax.set_xlabel('x1')
ax.set_ylabel('x2')
ax.set_zlabel('F')
Output:
Constrained:
status: 0
success: True
njev: 4
nfev: 4
fun: 7.9999999999997584
x: array([ 2., 3.])
message: 'Optimization terminated successfully.'
jac: array([ 4., -8., 0.])
nit: 4
Unconstrained:
status: 0
success: True
njev: 3
nfev: 5
fun: 0.0
x: array([ -2.66453526e-15, 4.00000000e+00])
message: 'Optimization terminated successfully.'
jac: array([ -5.32907052e-15, -3.55271368e-15, 0.00000000e+00])
nit: 3
#3
9
This might be a late answer, but I found CVXOPT - http://cvxopt.org/ - as the commonly used free python library for Quadratic Programming. However, it is not easy to install, as it requires the installation of other dependencies.
这可能是一个迟到的答案,但我发现CVXOPT - http://cvxopt.org/ - 作为二次规划常用的免费python库。但是,安装起来并不容易,因为它需要安装其他依赖项。
#4
3
mystic provides a pure python implementation of nonlinear/non-convex optimization algorithms with advanced constraints functionality that typically is only found in QP solvers. mystic actually provides more robust constraints than most QP solvers. However, if you are looking for optimization algorithmic speed, then the following is not for you. mystic is not slow, but it's pure python as opposed to python bindings to C. If you are looking for flexibility and QP constraints functionality in a nonlinear solver, then you might be interested.
mystic提供了非线性/非凸优化算法的纯python实现,具有高级约束功能,通常只能在QP求解器中找到。 mystic实际上提供了比大多数QP求解器更强大的约束。但是,如果您正在寻找优化算法速度,那么以下内容不适合您。 mystic并不慢,但它是纯python而不是python绑定到C.如果你在非线性求解器中寻找灵活性和QP约束功能,那么你可能会感兴趣。
"""
Maximize: f = 2*x[0]*x[1] + 2*x[0] - x[0]**2 - 2*x[1]**2
Subject to: -2*x[0] + 2*x[1] <= -2
2*x[0] - 4*x[1] <= 0
x[0]**3 -x[1] == 0
where: 0 <= x[0] <= inf
1 <= x[1] <= inf
"""
import numpy as np
import mystic.symbolic as ms
import mystic.solvers as my
import mystic.math as mm
# generate constraints and penalty for a nonlinear system of equations
ieqn = '''
-2*x0 + 2*x1 <= -2
2*x0 - 4*x1 <= 0'''
eqn = '''
x0**3 - x1 == 0'''
cons = ms.generate_constraint(ms.generate_solvers(ms.simplify(eqn,target='x1')))
pens = ms.generate_penalty(ms.generate_conditions(ieqn), k=1e3)
bounds = [(0., None), (1., None)]
# get the objective
def objective(x, sign=1):
x = np.asarray(x)
return sign * (2*x[0]*x[1] + 2*x[0] - x[0]**2 - 2*x[1]**2)
# solve
x0 = np.random.rand(2)
sol = my.fmin_powell(objective, x0, constraint=cons, penalty=pens, disp=True,
bounds=bounds, gtol=3, ftol=1e-6, full_output=True,
args=(-1,))
print 'x* = %s; f(x*) = %s' % (sol[0], -sol[1])
Things to note is that mystic can generically apply LP, QP, and higher order equality and inequality constraints to any given optimizer, not just a special QP solver. Secondly, mystic can digest symbolic math, so the ease of defining/entering the constraints is a bit nicer than working with the matrices and derivatives of functions. mystic depends on numpy, and will use scipy if it is installed (however, scipy is not required). mystic utilizes sympy to handle symbolic constraints, but it's also not required for optimization in general.
值得注意的是,神秘主义者可以将LP,QP和更高阶等式和不等式约束应用于任何给定的优化器,而不仅仅是一个特殊的QP求解器。其次,神秘主义者可以消化符号数学,因此定义/输入约束的容易性比使用函数的矩阵和导数更好。神秘主义取决于numpy,如果安装了scipy将使用scipy(但是,不需要scipy)。 mystic利用sympy处理符号约束,但一般来说也不需要优化。
Output:
Optimization terminated successfully.
Current function value: -2.000000
Iterations: 3
Function evaluations: 103
x* = [ 2. 1.]; f(x*) = 2.0
Get mystic here: https://github.com/uqfoundation
在这里获取神秘主义:https://github.com/uqfoundation