python 中约束非线性优化的推荐包是什么?
我要解决的具体问题是:
我有一个未知的X
(Nx1),我有M
(Nx1) u
向量和M
(NxN) s
矩阵。
max [5th percentile of (ui_T*X), i in 1 to M]
st
0<=X<=1 and
[95th percentile of (X_T*si*X), i in 1 to M]<= constant
当我开始解决这个问题时,我只有一个点估计 u
和 s
并且我能够用 cvxpy
解决上面的问题>。
我意识到,我没有对 u
和 s
进行一次估计,而是得到了整个值分布,所以我想更改我的目标函数,以便我可以使用整个分布。上面的问题描述是我尝试以有意义的方式包含该信息。
cvxpy
不能用来解决这个问题,我试过 scipy.optimize.anneal
,但我似乎无法设置未知值的界限。我也看过 pulp
,但它不允许非线性约束。
最佳答案
虽然 scipy.optimize.minimize
中的 SLSQP
算法很好,但它有很多限制。第一个是它是一个 QP
求解器,因此它适用于非常适合二次规划范式的方程。但是如果你有功能限制会发生什么?此外,scipy.optimize.minimize
不是全局优化器,因此您通常需要从非常接近最终结果的地方开始。
有一个受约束的非线性优化包(称为 mystic
)几乎与 scipy.optimize
本身一样长 - 我建议它作为处理任何一般约束非线性优化的首选。
例如,如果我理解你的伪代码,你的问题看起来像这样:
import numpy as np
M = 10
N = 3
Q = 10
C = 10
# let's be lazy, and generate s and u randomly...
s = np.random.randint(-Q,Q, size=(M,N,N))
u = np.random.randint(-Q,Q, size=(M,N))
def percentile(p, x):
x = np.sort(x)
p = 0.01 * p * len(x)
if int(p) != p:
return x[int(np.floor(p))]
p = int(p)
return x[p:p+2].mean()
def objective(x, p=5): # inverted objective, to find the max
return -1*percentile(p, [np.dot(np.atleast_2d(u[i]), x)[0] for i in range(0,M-1)])
def constraint(x, p=95, v=C): # 95%(xTsx) - v <= 0
x = np.atleast_2d(x)
return percentile(p, [np.dot(np.dot(x,s[i]),x.T)[0,0] for i in range(0,M-1)]) - v
bounds = [(0,1) for i in range(0,N)]
因此,要在 mystic
中处理您的问题,您只需指定边界和约束。
from mystic.penalty import quadratic_inequality
@quadratic_inequality(constraint, k=1e4)
def penalty(x):
return 0.0
from mystic.solvers import diffev2
from mystic.monitors import VerboseMonitor
mon = VerboseMonitor(10)
result = diffev2(objective, x0=bounds, penalty=penalty, npop=10, gtol=200, \
disp=False, full_output=True, itermon=mon, maxiter=M*N*100)
print result[0]
print result[1]
结果看起来像这样:
Generation 0 has Chi-Squared: -0.434718
Generation 10 has Chi-Squared: -1.733787
Generation 20 has Chi-Squared: -1.859787
Generation 30 has Chi-Squared: -1.860533
Generation 40 has Chi-Squared: -1.860533
Generation 50 has Chi-Squared: -1.860533
Generation 60 has Chi-Squared: -1.860533
Generation 70 has Chi-Squared: -1.860533
Generation 80 has Chi-Squared: -1.860533
Generation 90 has Chi-Squared: -1.860533
Generation 100 has Chi-Squared: -1.860533
Generation 110 has Chi-Squared: -1.860533
Generation 120 has Chi-Squared: -1.860533
Generation 130 has Chi-Squared: -1.860533
Generation 140 has Chi-Squared: -1.860533
Generation 150 has Chi-Squared: -1.860533
Generation 160 has Chi-Squared: -1.860533
Generation 170 has Chi-Squared: -1.860533
Generation 180 has Chi-Squared: -1.860533
Generation 190 has Chi-Squared: -1.860533
Generation 200 has Chi-Squared: -1.860533
Generation 210 has Chi-Squared: -1.860533
STOP("ChangeOverGeneration with {'tolerance': 0.005, 'generations': 200}")
[-0.17207128 0.73183465 -0.28218955]
-1.86053344078
mystic
非常灵活,可以处理任何类型的约束(例如等式、不等式),包括符号和功能约束。
我在上面将约束指定为“惩罚”,这是传统方式,因为它们在违反约束时对目标应用惩罚。
mystic
还提供非线性核变换,它通过减少有效解的空间(即通过空间映射或核变换)来限制解空间。
例如,这里的 mystic
解决了一个问题,该问题破坏了许多 QP 求解器,因为约束不是约束矩阵的形式。它正在优化压力容器的设计。
"Pressure Vessel Design"
def objective(x):
x0,x1,x2,x3 = x
return 0.6224*x0*x2*x3 + 1.7781*x1*x2**2 + 3.1661*x0**2*x3 + 19.84*x0**2*x2
bounds = [(0,1e6)]*4
# with penalty='penalty' applied, solution is:
xs = [0.72759093, 0.35964857, 37.69901188, 240.0]
ys = 5804.3762083
from mystic.symbolic import generate_constraint, generate_solvers, simplify
from mystic.symbolic import generate_penalty, generate_conditions
equations = """
-x0 + 0.0193*x2 <= 0.0
-x1 + 0.00954*x2 <= 0.0
-pi*x2**2*x3 - (4/3.)*pi*x2**3 + 1296000.0 <= 0.0
x3 - 240.0 <= 0.0
"""
cf = generate_constraint(generate_solvers(simplify(equations)))
pf = generate_penalty(generate_conditions(equations), k=1e12)
if __name__ == '__main__':
from mystic.solvers import diffev2
from mystic.math import almostEqual
from mystic.monitors import VerboseMonitor
mon = VerboseMonitor(10)
result = diffev2(objective, x0=bounds, bounds=bounds, constraints=cf, penalty=pf, \
npop=40, gtol=50, disp=False, full_output=True, itermon=mon)
assert almostEqual(result[0], xs, rel=1e-2)
assert almostEqual(result[1], ys, rel=1e-2)
在这里找到这个,大约有 100 个类似的例子:https://github.com/uqfoundation/mystic .
我是作者,所以我有点偏见。然而,偏差非常轻微。 mystic
既成熟又得到了很好的支持,在解决硬约束非线性优化问题方面的能力无与伦比。
关于Python 约束非线性优化,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/21765794/