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我正在尝试解决类似于下面描述的玩具示例的优化问题。正如评论中所指出的，我目前使用 scipy 的实现太慢了，而且似乎没有收敛。我如何获得一个体面的解决方案？你可以使用 scipy、mystic 或任何你认为合适的库。

请注意，我不需要全局最小值，搜索可以立即停止loss(X) <= 1。现实世界的目标主要是用 SQL 编写的，因此速度非常慢，所以我还希望优化在loss被评估约 200 次时终止。（这不是硬性要求，您也可以在优化运行 5 分钟后终止。）

虽然这个问题类似于Minimizing non-convex function with linear constraint 和 bound in mystic，但它绝对不是重复的。这两个问题甚至没有处理相同的目标。

import numpy as np
from scipy.optimize import differential_evolution, LinearConstraint


# Inhabitants in a rural city are voting for the name of a newborn. The city houses 40 dwarves
# and 10 humans in total, and there are 100 candidate names to vote for.
dwarf_population, human_population = 40, 10
population = dwarf_population + human_population
candidate_count = 100

# Each inhabitant has different number of votes in their hand.
scores_per_citizen = np.random.randint(15, 20, population)

# Let's say the original result looks like this. (Yes, one can cast a fraction of their votes)
alpha = np.abs(np.random.normal(size=candidate_count))
scores = np.diag(scores_per_citizen).dot(np.random.dirichlet(alpha, population))
assert np.allclose(scores.sum(1), scores_per_citizen)


# Here is how the votes are counted.
def count(scores_: np.ndarray) -> np.ndarray:
    # Dwarves have a weird tradition: the top 10 popular names chosen by dwarves will get all their votes.
    # (I guess this is what makes our objective non-convex.)
    scores_by_dwarves = scores_[0:40, :]
    score_per_candidate_by_dwarves_raw = scores_by_dwarves.sum(1)
    top_10_popular_name_indices = np.argsort(-score_per_candidate_by_dwarves_raw)[:10]
    score_per_candidate_by_dwarves = np.zeros(candidate_count)
    score_per_candidate_by_dwarves[top_10_popular_name_indices] = score_per_candidate_by_dwarves_raw[
        top_10_popular_name_indices]
    score_per_candidate_by_dwarves = scores_by_dwarves.sum() \
                                     * score_per_candidate_by_dwarves \
                                     / score_per_candidate_by_dwarves.sum()
    assert np.allclose(score_per_candidate_by_dwarves.sum(), score_per_candidate_by_dwarves_raw.sum())

    # Humans, on the other hand, just adds everyone's votes together.
    scores_by_humans = scores_[40:, :]
    scores_per_candidate_by_humans = scores_by_humans.sum(0)

    # The final result is the sum of the scores by dwarves and humans.
    return score_per_candidate_by_dwarves + scores_per_candidate_by_humans


# So this is the legitimate result.
scores_per_candidate = count(scores)

# Now, you want to cheat a bit and make your proposal (assuming it's the first one) the most popular one.
target_scores_per_candidate = scores_per_candidate.copy()
argmax = scores_per_candidate.argmax()
target_scores_per_candidate[argmax] = scores_per_candidate[0]
target_scores_per_candidate[0] = scores_per_candidate[argmax]
assert np.allclose(scores_per_candidate.sum(), target_scores_per_candidate.sum())

# However, you cannot just manipulate the result, otherwise the auditors will find out!
# Instead, the raw scores must be manipulated such that your submission ranks top among others.
# You decide to solve for a multiplier to the original scores.
init_coef = np.ones_like(scores).reshape(-1)


# In other words, your goal is to find the minimum of the following objective.
def loss(coef_: np.ndarray) -> float:
    scores_ = scores * coef_.reshape(scores.shape)
    scores_per_candidate_ = count(scores_)
    return ((scores_per_candidate_ - scores_per_candidate) ** 2).sum()


# This is a helper constant matrix. Ignore it for now.
A = np.concatenate([np.tile(np.concatenate([np.repeat(1, candidate_count),
                                            np.repeat(0, population * candidate_count)]),
                            population - 1),
                    np.repeat(1, candidate_count)])
A = A.reshape((population, population * candidate_count))


# Meanwhile, some constraints must hold.
def constraints(coef_: np.ndarray):
    # The total votes of each citizen must not change.
    coef_reshaped = coef_.reshape((population, candidate_count))
    assert np.allclose((coef_reshaped * scores).sum(1), scores_per_citizen)

    # Another way to express the same thing with matrices.
    assert np.allclose(A.dot(np.diag(scores.reshape(-1))).dot(coef_), scores_per_citizen)

    # Additionally, all scores must be positive.
    assert np.all(coef_reshaped * scores >= 0)


# Let's express the constraint with a LinearConstraint.
score_match_quota = LinearConstraint(A.dot(np.diag(scores.reshape(-1))), scores_per_citizen, scores_per_citizen)

# Run optimization (Spoiler: this is extremely slow, and doesn't seem to converge)
rv = differential_evolution(loss,
                            bounds=[(0, 1000)] * init_coef.size,  # the 1000 here is superficial and can be replaced by other large numbers
                            init=np.vstack((init_coef, init_coef, init_coef, init_coef, init_coef)),
                            constraints=score_match_quota)

# Sanity check
constraints(rv.x)
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1 回答 1

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答案与您引用的问题几乎相同......但是，我必须进一步考虑一些限制来证明这一点。让我重写你的代码——只是使用一些较短的名称。

>>> import mystic as my
>>> import numpy as np
>>> 
>>> # Inhabitants in a rural city are voting for the name of a newborn.
... # The city houses 40 dwarves and 10 humans in total, and there are
... # 100 candidate names to vote for.
... dwarves, humans = 40, 10
>>> citizens = dwarves + humans
>>> names = 100
>>> # Each inhabitant has different number of votes in their hand.
... votes_per_citizen = np.random.randint(15, 20, citizens)
>>> 
>>> # Let's say the original result looks like this.
... # (Yes, one can cast a fraction of their votes)
... alpha = np.abs(np.random.normal(size=names))
>>> votes = np.diag(votes_per_citizen).dot(np.random.dirichlet(alpha, citizens))
>>> # NOTE: assert np.allclose(votes.sum(1), votes_per_citizen)
... 
>>> # Here is how the votes are counted.
... def count(votes): #NOTE: votes.shape is (citizens, names)
...     # Dwarves have a weird tradition: the top 10 popular names chosen
...     # by dwarves will get all their votes.
...     # (I guess this is what makes our objective non-convex.)
...     dwarf_votes = votes[:dwarves]
...     dwarf_votes_per_name_ = dwarf_votes.sum(1)
...     top_10_idx = np.argsort(-dwarf_votes_per_name_)[:10]
...     dwarf_votes_per_name = np.zeros(names)
...     dwarf_votes_per_name[top_10_idx] = dwarf_votes_per_name_[top_10_idx]
...     dwarf_votes_per_name = \
...         dwarf_votes.sum() * dwarf_votes_per_name / dwarf_votes_per_name.sum()
...     #NOTE: assert np.allclose(dwarf_votes_per_name.sum(), dwarf_votes_per_name_.sum())
...     # Humans, on the other hand, just add everyone's votes together.
...     human_votes = votes[dwarves:]
...     human_votes_per_name = human_votes.sum(0)
...     # The final result is the sum of the scores by dwarves and humans.
...     return dwarf_votes_per_name + human_votes_per_name #NOTE: shape = (names,)
... 
>>> # So this is the legitimate result.
... votes_per_name = count(votes)
>>> 
>>> # Now, you want to cheat a bit and make your proposal
... # (assuming it's the first one) the most popular one.
... votes_per_name_ = votes_per_name.copy()
>>> argmax = votes_per_name.argmax()
>>> votes_per_name_[argmax] = votes_per_name[0]
>>> votes_per_name_[0] = votes_per_name[argmax]
>>> #NOTE: assert np.allclose(votes_per_name.sum(), votes_per_name_.sum())
... 
>>> # However, you cannot just manipulate the result, otherwise the auditors
... # will find out! Instead, the raw scores must be manipulated such that your
... # submission ranks top among others.  You decide to solve for a multiplier
... # to the original scores.
... coef = np.ones_like(votes).reshape(-1)
>>> 
>>> # In other words, your goal is to find the minimum of the following objective.
... def loss(coef): #NOTE: coef.shape is (citizens*votes,)
...     votes_ = votes * coef.reshape(votes.shape)
...     votes_per_name_ = count(votes_)
...     return ((votes_per_name_ - votes_per_name)**2).sum()
... 
>>> 
>>> # This is a helper constant matrix. Ignore it for now.
... A = np.concatenate([np.tile(np.concatenate([np.repeat(1, names), np.repeat(0, citizens * names)]), citizens - 1), np.repeat(1, names)]).reshape((citizens, citizens * names))
>>> A_ = A.dot(np.diag(votes.reshape(-1)))
>>>

到目前为止，代码与您的问题相同。现在，让我们使用一些神秘主义者的工具。

首先，让我们把它放在你提到的问题的背景下。

>>> # Build constraints
... cons = my.symbolic.linear_symbolic(A_, votes_per_citizen)
>>> cons = my.symbolic.solve(cons) #NOTE: this may take a while...
>>> cons = my.symbolic.generate_constraint(my.symbolic.generate_solvers(cons))
>>> 
>>> def cons_(x): #NOTE: preserve x
...     return cons(x.copy())
... 
>>>

现在，就像在引用的问题中一样，我们可以使用像fmin_powellwith这样的简单求解器constraints=cons_来解决它。但是，我发现 mystic 在这里会有些挣扎，正如我将展示的那样。

>>> bounds = [(0, 1000)] * coef.size
>>> x0 = np.random.randint(0, 1000, coef.shape).astype(float).tolist()
>>>
>>> stepmon = my.monitors.VerboseMonitor(1)
>>> rv = my.solvers.fmin_powell(loss, x0=x0, bounds=bounds, itermon=stepmon,
...                             constraints=cons_,
...                             disp=1, maxfun=20000, gtol=None, ftol=500)
Generation 0 has ChiSquare: inf
Generation 1 has ChiSquare: inf
Generation 2 has ChiSquare: inf
Generation 3 has ChiSquare: inf
Generation 4 has ChiSquare: inf
Generation 5 has ChiSquare: inf
Generation 6 has ChiSquare: inf
Generation 7 has ChiSquare: inf
...

我已经杀死了这里的优化......当你看到inf这意味着神秘主义者正在努力解决约束。

问题是应该有额外的约束来帮助它，即A_.dot(cons_(x0))应该是整数。此外，最好更好地控制底片的存在。

>>> np.round(A_.dot(cons_(x0)), 10) - votes_per_citizen
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0.])
>>> cons_(x0)[:5]
[-574537.1464429945, 98.0, 326.0, 114.0, 694.0]

事实上，每一个citizens元素都是负数。

所以，我尝试使用惩罚来代替。

>>> def penalty1(x):
...     return np.abs((np.array(x).reshape(citizens, names) * votes).sum(1) - votes_per_citizen).sum()
... 
>>> def penalty2(x):
...     return np.abs(A_.dot(x) - votes_per_citizen).sum()
... 
>>> def penalty3(x):
...     return np.abs((np.array(x).reshape(citizens, names) * votes).min(1).sum())
... 
>>> #FIXME: utilize that A_.dot(x) should be integer-valued
... 
>>> @my.penalty.quadratic_equality(penalty2)
... @my.penalty.quadratic_equality(penalty3)
... def penalty(x):
...     return 0.0
... 
>>> 
>>> bounds = [(0, 1000)] * coef.size
>>> x0 = np.random.randint(0, 1000, coef.shape).astype(float).tolist()
>>> 
>>> stepmon = my.monitors.VerboseMonitor(1)
>>> 
>>> rv = my.solvers.fmin_powell(loss, x0=x0, bounds=bounds, itermon=stepmon,
...                             penalty=penalty, # constraints=cons_,
...                             disp=1, maxfun=20000, gtol=None, ftol=500)
Generation 0 has ChiSquare: 4316466725165.325684
Generation 1 has ChiSquare: 97299808.307906
Generation 2 has ChiSquare: 1125.438322
Generation 3 has ChiSquare: 1116.735393
Warning: Maximum number of function evaluations has been exceeded.
STOP("EvaluationLimits with {'evaluations': 20000, 'generations': 500000}")
>>>

这似乎工作得很好（注意我使用了 20000 evals 而不是 200，当 loss 是 500 而不是 1 时停止使用 ftol）。rv非常接近，并且优化相当快。

>>> penalty(rv)
4.979032041874226
>>> np.round(A_.dot(rv), 2) - votes_per_citizen
array([0.  , 0.22, 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,
       0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,
       0.  , 0.  , 0.  ])
>>> 
>>> penalty(x0)
4313023244843.466
>>> np.round(A_.dot(x0), 2) - votes_per_citizen
array([ 6996.61,  5872.2 ,  6398.82,  7593.65,  9137.81, 10347.84,
        9204.44,  6160.77,  9626.64,  7572.4 , 10771.24,  8673.7 ,
       10212.18,  7581.45,  5097.13,  7631.2 ,  8274.92,  9684.09,
        9504.27,  9067.73,  7332.77, 10214.02,  8255.38,  9853.74,
        6613.19])

在这里，A_.dot(rv)不是那么准确（四舍五入到 2 个位置而不是 10 个）......并且将再次受益于制作A_.dot(rv)整数的约束。

我将把它留给未来的例子。

于 2021-01-17T23:05:21.607 回答