CODEWITHSUNDEEP
optimizationpytorchclampnon-convex
Capeboom
Capeboom

Reputation: 515

How to apply bounds on a variable when performing optimisation in Pytorch?

I am trying to use Pytorch for non-convex optimisation, trying to maximise my objective (so minimise in SGD). I would like to bound my dependent variable x > 0, and also have the sum of my x values be less than 1000.

I think I have the penalty implemented correctly in the form of a ramp penalty, but am struggling with the bounding of the x variable. In Pytorch you can set the bounds using clamp but it doesn't seem appropriate in this case. I think this is because optim needs the gradients free under the hood. Full working example: 

import torch
from torch.autograd import Variable
import numpy as np

def objective(x, a, b, c):   # Want to maximise this quantity (so minimise in SGD)
    d = 1 / (1 + torch.exp(-a * (x)))

    # Checking constraint 
    exceeded_limit = constraint(x).item()
    #print(exceeded_limit)

    obj = torch.sum(d * (b * c - x))

    # If overlimit add ramp penalty
    if  exceeded_limit < 0:
        obj = obj - (exceeded_limit * 10)
        print("Exceeded limit")

    return - obj

def constraint(x, limit = 1000): # Must be > 0
    return limit - x.sum()

N = 1000

# x is variable to optimise for 
x = Variable(torch.Tensor([1 for ii in range(N)]), requires_grad=True)
a = Variable(torch.Tensor(np.random.uniform(0,100,N)), requires_grad=True)
b = Variable(torch.Tensor(np.random.rand(N)), requires_grad=True)
c = Variable(torch.Tensor(np.random.rand(N)), requires_grad=True)

# Would like to include the clamp
# x = torch.clamp(x, min=0)

# Non-convex methodf
opt = torch.optim.SGD([x], lr=.01)

for i in range(10000):
    # Zeroing gradients
    opt.zero_grad()

    # Evaluating the objective
    obj = objective(x, a, b, c)

    # Calculate gradients
    obj.backward() 
    opt.step()
    if i%1000==0:  print("Objective: %.1f" % -obj.item())

print("\nObjective: {}".format(-obj))
print("Limit: {}".format(constraint(x).item()))

if torch.sum(x<0) > 0: print("Bounds not met")
if  constraint(x).item() < 0: print("Constraint not met")

Any suggestions as to how to impose the bounds would be appreciated, either using clamp or otherwise. Or generally advice on non-convex optimisation using Pytorch. This is a much simpler and scaled down version of the problem I'm working so am trying to find a lightweight solution if possible. I am considering using a workaround such as transforming the x variable using an exponential function but then you'd have to scale the function to avoid the positive values becoming infinite, and I want some flexibility with being able to set the constraint.

Upvotes: 4

Views: 6905

Answers (1)

1943 Yonv
1943 Yonv

Reputation: 310

I meet the same problem with you. I want to apply bounds on a variable in PyTorch, too. And I solved this problem by the below Way3.

Your example is a little compliex but I am still learning English. So I give a simpler example below.

For example, there is a trainable variable v, its bounds is (-1, 1)

v = torch.tensor((0.5, ）, require_grad=True)
v_loss = xxxx
optimizer.zero_grad()
v_loss.backward()
optimizer.step()

Way1. RuntimeError: a leaf Variable that requires grad has been used in an in-place operation.

v.clamp_(-1, 1)

Way2. RuntimeError: Trying to backward through the graph a second time, but the buffers have already been freed.

v = torch.clamp(v, -1, +1)  # equal to v = v.clamp(-1, +1)

Way3. NotError. I solved this problem in Way3.

with torch.no_grad():
    v[:] = v.clamp(-1, +1)  # You must use v[:]=xxx instead of v=xxx

Upvotes: 6

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