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Up to now, all `torch`

use instances we’ve mentioned right here have been in deep studying. Nevertheless, its computerized differentiation function is helpful in different areas. One distinguished instance is numerical optimization: We are able to use `torch`

to search out the minimal of a operate.

In actual fact, operate minimization is *precisely* what occurs in coaching a neural community. However there, the operate in query usually is much too complicated to even think about discovering its minima analytically. Numerical optimization goals at build up the instruments to deal with simply this complexity. To that finish, nevertheless, it begins from capabilities which can be far much less deeply composed. As an alternative, they’re hand-crafted to pose particular challenges.

This publish is a primary introduction to numerical optimization with `torch`

. Central takeaways are the existence and usefulness of its L-BFGS optimizer, in addition to the influence of working L-BFGS with line search. As a enjoyable add-on, we present an instance of constrained optimization, the place a constraint is enforced by way of a quadratic penalty operate.

To heat up, we take a detour, minimizing a operate “ourselves” utilizing nothing however tensors. It will develop into related later, although, as the general course of will nonetheless be the identical. All adjustments shall be associated to integration of `optimizer`

s and their capabilities.

## Operate minimization, DYI strategy

To see how we will reduce a operate “by hand”, let’s attempt the enduring Rosenbrock operate. This can be a operate with two variables:

[

f(x_1, x_2) = (a – x_1)^2 + b * (x_2 – x_1^2)^2

]

, with (a) and (b) configurable parameters typically set to 1 and 5, respectively.

In R:

Its minimal is situated at (1,1), inside a slender valley surrounded by breakneck-steep cliffs:

Our objective and technique are as follows.

We need to discover the values (x_1) and (x_2) for which the operate attains its minimal. We now have to begin someplace; and from wherever that will get us on the graph we comply with the damaging of the gradient “downwards”, descending into areas of consecutively smaller operate worth.

Concretely, in each iteration, we take the present ((x1,x2)) level, compute the operate worth in addition to the gradient, and subtract some fraction of the latter to reach at a brand new ((x1,x2)) candidate. This course of goes on till we both attain the minimal – the gradient is zero – or enchancment is under a selected threshold.

Right here is the corresponding code. For no particular causes, we begin at `(-1,1)`

. The training fee (the fraction of the gradient to subtract) wants some experimentation. (Attempt 0.1 and 0.001 to see its influence.)

```
num_iterations <- 1000
# fraction of the gradient to subtract
lr <- 0.01
# operate enter (x1,x2)
# that is the tensor w.r.t. which we'll have torch compute the gradient
x_star <- torch_tensor(c(-1, 1), requires_grad = TRUE)
for (i in 1:num_iterations) {
if (i %% 100 == 0) cat("Iteration: ", i, "n")
# name operate
worth <- rosenbrock(x_star)
if (i %% 100 == 0) cat("Worth is: ", as.numeric(worth), "n")
# compute gradient of worth w.r.t. params
worth$backward()
if (i %% 100 == 0) cat("Gradient is: ", as.matrix(x_star$grad), "nn")
# guide replace
with_no_grad({
x_star$sub_(lr * x_star$grad)
x_star$grad$zero_()
})
}
```

```
Iteration: 100
Worth is: 0.3502924
Gradient is: -0.667685 -0.5771312
Iteration: 200
Worth is: 0.07398106
Gradient is: -0.1603189 -0.2532476
...
...
Iteration: 900
Worth is: 0.0001532408
Gradient is: -0.004811743 -0.009894371
Iteration: 1000
Worth is: 6.962555e-05
Gradient is: -0.003222887 -0.006653666
```

Whereas this works, it actually serves for instance the precept. With `torch`

offering a bunch of confirmed optimization algorithms, there is no such thing as a want for us to manually compute the candidate (mathbf{x}) values.

## Operate minimization with `torch`

optimizers

As an alternative, we let a `torch`

optimizer replace the candidate (mathbf{x}) for us. Habitually, our first attempt is *Adam*.

### Adam

With Adam, optimization proceeds so much quicker. Reality be informed, although, selecting a great studying fee *nonetheless* takes non-negligeable experimentation. (Attempt the default studying fee, 0.001, for comparability.)

```
num_iterations <- 100
x_star <- torch_tensor(c(-1, 1), requires_grad = TRUE)
lr <- 1
optimizer <- optim_adam(x_star, lr)
for (i in 1:num_iterations) {
if (i %% 10 == 0) cat("Iteration: ", i, "n")
optimizer$zero_grad()
worth <- rosenbrock(x_star)
if (i %% 10 == 0) cat("Worth is: ", as.numeric(worth), "n")
worth$backward()
optimizer$step()
if (i %% 10 == 0) cat("Gradient is: ", as.matrix(x_star$grad), "nn")
}
```

```
Iteration: 10
Worth is: 0.8559565
Gradient is: -1.732036 -0.5898831
Iteration: 20
Worth is: 0.1282992
Gradient is: -3.22681 1.577383
...
...
Iteration: 90
Worth is: 4.003079e-05
Gradient is: -0.05383469 0.02346456
Iteration: 100
Worth is: 6.937736e-05
Gradient is: -0.003240437 -0.006630421
```

It took us a couple of hundred iterations to reach at an honest worth. This can be a lot quicker than the guide strategy above, however nonetheless quite a bit. Fortunately, additional enhancements are attainable.

### L-BFGS

Among the many many `torch`

optimizers generally utilized in deep studying (Adam, AdamW, RMSprop …), there’s one “outsider”, significantly better identified in traditional numerical optimization than in neural-networks house: L-BFGS, a.okay.a. Restricted-memory BFGS, a memory-optimized implementation of the Broyden–Fletcher–Goldfarb–Shanno optimization algorithm (BFGS).

BFGS is maybe essentially the most extensively used among the many so-called Quasi-Newton, second-order optimization algorithms. Versus the household of first-order algorithms that, in deciding on a descent path, make use of gradient info solely, second-order algorithms moreover take curvature info under consideration. To that finish, precise Newton strategies really compute the Hessian (a pricey operation), whereas Quasi-Newton strategies keep away from that value and, as a substitute, resort to iterative approximation.

Wanting on the contours of the Rosenbrock operate, with its extended, slender valley, it’s not troublesome to think about that curvature info would possibly make a distinction. And, as you’ll see in a second, it actually does. Earlier than although, one be aware on the code. When utilizing L-BFGS, it’s essential to wrap each operate name and gradient analysis in a closure (`calc_loss()`

, within the under snippet), for them to be callable a number of occasions per iteration. You’ll be able to persuade your self that the closure is, actually, entered repeatedly, by inspecting this code snippet’s chatty output:

```
num_iterations <- 3
x_star <- torch_tensor(c(-1, 1), requires_grad = TRUE)
optimizer <- optim_lbfgs(x_star)
calc_loss <- operate() {
optimizer$zero_grad()
worth <- rosenbrock(x_star)
cat("Worth is: ", as.numeric(worth), "n")
worth$backward()
cat("Gradient is: ", as.matrix(x_star$grad), "nn")
worth
}
for (i in 1:num_iterations) {
cat("Iteration: ", i, "n")
optimizer$step(calc_loss)
}
```

```
Iteration: 1
Worth is: 4
Gradient is: -4 0
Worth is: 6
Gradient is: -2 10
...
...
Worth is: 0.04880721
Gradient is: -0.262119 -0.1132655
Worth is: 0.0302862
Gradient is: 1.293824 -0.7403332
Iteration: 2
Worth is: 0.01697086
Gradient is: 0.3468466 -0.3173429
Worth is: 0.01124081
Gradient is: 0.2420997 -0.2347881
...
...
Worth is: 1.111701e-09
Gradient is: 0.0002865837 -0.0001251698
Worth is: 4.547474e-12
Gradient is: -1.907349e-05 9.536743e-06
Iteration: 3
Worth is: 4.547474e-12
Gradient is: -1.907349e-05 9.536743e-06
```

Although we ran the algorithm for 3 iterations, the optimum worth actually is reached after two. Seeing how nicely this labored, we attempt L-BFGS on a harder operate, named *flower*, for fairly self-evident causes.

## (But) extra enjoyable with L-BFGS

Right here is the *flower* operate. Mathematically, its minimal is close to `(0,0)`

, however technically the operate itself is undefined at `(0,0)`

, for the reason that `atan2`

used within the operate shouldn’t be outlined there.

```
a <- 1
b <- 1
c <- 4
flower <- operate(x) {
a * torch_norm(x) + b * torch_sin(c * torch_atan2(x[2], x[1]))
}
```

We run the identical code as above, ranging from `(20,20)`

this time.

```
num_iterations <- 3
x_star <- torch_tensor(c(20, 0), requires_grad = TRUE)
optimizer <- optim_lbfgs(x_star)
calc_loss <- operate() {
optimizer$zero_grad()
worth <- flower(x_star)
cat("Worth is: ", as.numeric(worth), "n")
worth$backward()
cat("Gradient is: ", as.matrix(x_star$grad), "n")
cat("X is: ", as.matrix(x_star), "nn")
worth
}
for (i in 1:num_iterations) {
cat("Iteration: ", i, "n")
optimizer$step(calc_loss)
}
```

```
Iteration: 1
Worth is: 28.28427
Gradient is: 0.8071069 0.6071068
X is: 20 20
...
...
Worth is: 19.33546
Gradient is: 0.8100872 0.6188223
X is: 12.957 14.68274
...
...
Worth is: 18.29546
Gradient is: 0.8096464 0.622064
X is: 12.14691 14.06392
...
...
Worth is: 9.853705
Gradient is: 0.7546976 0.7025688
X is: 5.763702 8.895616
Worth is: 2635.866
Gradient is: -0.7407354 -0.6717985
X is: -1949.697 -1773.551
Iteration: 2
Worth is: 1333.113
Gradient is: -0.7413024 -0.6711776
X is: -985.4553 -897.5367
Worth is: 30.16862
Gradient is: -0.7903821 -0.6266789
X is: -21.02814 -21.72296
Worth is: 1281.39
Gradient is: 0.7544561 0.6563575
X is: 964.0121 843.7817
Worth is: 628.1306
Gradient is: 0.7616636 0.6480014
X is: 475.7051 409.7372
Worth is: 4965690
Gradient is: -0.7493951 -0.662123
X is: -3721262 -3287901
Worth is: 2482306
Gradient is: -0.7503822 -0.6610042
X is: -1862675 -1640817
Worth is: 8.61863e+11
Gradient is: 0.7486113 0.6630091
X is: 645200412672 571423064064
Worth is: 430929412096
Gradient is: 0.7487153 0.6628917
X is: 322643460096 285659529216
Worth is: Inf
Gradient is: 0 0
X is: -2.826342e+19 -2.503904e+19
Iteration: 3
Worth is: Inf
Gradient is: 0 0
X is: -2.826342e+19 -2.503904e+19
```

This has been much less of a hit. At first, loss decreases properly, however abruptly, the estimate dramatically overshoots, and retains bouncing between damaging and optimistic outer house ever after.

Fortunately, there’s something we will do.

### L-BFGS with line search

Taken in isolation, what a Quasi-Newton technique like L-BFGS does is decide the most effective descent path. Nevertheless, as we simply noticed, a great path shouldn’t be sufficient. With the flower operate, wherever we’re, the optimum path results in catastrophe if we keep on it lengthy sufficient. Thus, we’d like an algorithm that rigorously evaluates not solely the place to go, but additionally, how far.

Because of this, L-BFGS implementations generally incorporate *line search*, that’s, a algorithm indicating whether or not a proposed step size is an efficient one, or needs to be improved upon.

Particularly, `torch`

’s L-BFGS optimizer implements the Robust Wolfe circumstances. We re-run the above code, altering simply two strains. Most significantly, the one the place the optimizer is instantiated:

`optimizer <- optim_lbfgs(x_star, line_search_fn = "strong_wolfe")`

And secondly, this time I discovered that after the third iteration, loss continued to lower for some time, so I let it run for 5 iterations. Right here is the output:

```
Iteration: 1
...
...
Worth is: -0.8838741
Gradient is: 3.742207 7.521572
X is: 0.09035123 -0.03220009
Worth is: -0.928809
Gradient is: 1.464702 0.9466625
X is: 0.06564617 -0.026706
Iteration: 2
...
...
Worth is: -0.9991404
Gradient is: 39.28394 93.40318
X is: 0.0006493925 -0.0002656128
Worth is: -0.9992246
Gradient is: 6.372203 12.79636
X is: 0.0007130796 -0.0002947929
Iteration: 3
...
...
Worth is: -0.9997789
Gradient is: 3.565234 5.995832
X is: 0.0002042478 -8.457939e-05
Worth is: -0.9998025
Gradient is: -4.614189 -13.74602
X is: 0.0001822711 -7.553725e-05
Iteration: 4
...
...
Worth is: -0.9999917
Gradient is: -382.3041 -921.4625
X is: -6.320081e-06 2.614706e-06
Worth is: -0.9999923
Gradient is: -134.0946 -321.2681
X is: -6.921942e-06 2.865841e-06
Iteration: 5
...
...
Worth is: -0.9999999
Gradient is: -3446.911 -8320.007
X is: -7.267168e-08 3.009783e-08
Worth is: -0.9999999
Gradient is: -3419.361 -8253.501
X is: -7.404627e-08 3.066708e-08
```

It’s nonetheless not good, however so much higher.

Lastly, let’s go one step additional. Can we use `torch`

for constrained optimization?

### Quadratic penalty for constrained optimization

In constrained optimization, we nonetheless seek for a minimal, however that minimal can’t reside simply anyplace: Its location has to satisfy some variety of extra circumstances. In optimization lingo, it must be *possible*.

As an example, we stick with the flower operate, however add on a constraint: (mathbf{x}) has to lie exterior a circle of radius (sqrt(2)), centered on the origin. Formally, this yields the inequality constraint

[

2 – {x_1}^2 – {x_2}^2 <= 0

]

A option to reduce *flower* and but, on the identical time, honor the constraint is to make use of a penalty operate. With penalty strategies, the worth to be minimized is a sum of two issues: the goal operate’s output and a penalty reflecting potential constraint violation. Use of a *quadratic* *penalty*, for instance, ends in including a a number of of the sq. of the constraint operate’s output:

```
# x^2 + y^2 >= 2
# 2 - x^2 - y^2 <= 0
constraint <- operate(x) 2 - torch_square(torch_norm(x))
# quadratic penalty
penalty <- operate(x) torch_square(torch_max(constraint(x), different = 0))
```

A priori, we will’t know the way large that a number of must be to implement the constraint. Subsequently, optimization proceeds iteratively. We begin with a small multiplier, (1), say, and enhance it for so long as the constraint remains to be violated:

```
penalty_method <- operate(f, p, x, k_max, rho = 1, gamma = 2, num_iterations = 1) {
for (okay in 1:k_max) {
cat("Beginning step: ", okay, ", rho = ", rho, "n")
reduce(f, p, x, rho, num_iterations)
cat("Worth: ", as.numeric(f(x)), "n")
cat("X: ", as.matrix(x), "n")
current_penalty <- as.numeric(p(x))
cat("Penalty: ", current_penalty, "n")
if (current_penalty == 0) break
rho <- rho * gamma
}
}
```

`reduce()`

, known as from `penalty_method()`

, follows the same old proceedings, however now it minimizes the sum of the goal and up-weighted penalty operate outputs:

```
reduce <- operate(f, p, x, rho, num_iterations) {
calc_loss <- operate() {
optimizer$zero_grad()
worth <- f(x) + rho * p(x)
worth$backward()
worth
}
for (i in 1:num_iterations) {
cat("Iteration: ", i, "n")
optimizer$step(calc_loss)
}
}
```

This time, we begin from a low-target-loss, however unfeasible worth. With yet one more change to default L-BFGS (specifically, a lower in tolerance), we see the algorithm exiting efficiently after twenty-two iterations, on the level `(0.5411692,1.306563)`

.

```
x_star <- torch_tensor(c(0.5, 0.5), requires_grad = TRUE)
optimizer <- optim_lbfgs(x_star, line_search_fn = "strong_wolfe", tolerance_change = 1e-20)
penalty_method(flower, penalty, x_star, k_max = 30)
```

```
Beginning step: 1 , rho = 1
Iteration: 1
Worth: 0.3469974
X: 0.5154735 1.244463
Penalty: 0.03444662
Beginning step: 2 , rho = 2
Iteration: 1
Worth: 0.3818618
X: 0.5288152 1.276674
Penalty: 0.008182613
Beginning step: 3 , rho = 4
Iteration: 1
Worth: 0.3983252
X: 0.5351116 1.291886
Penalty: 0.001996888
...
...
Beginning step: 20 , rho = 524288
Iteration: 1
Worth: 0.4142133
X: 0.5411959 1.306563
Penalty: 3.552714e-13
Beginning step: 21 , rho = 1048576
Iteration: 1
Worth: 0.4142134
X: 0.5411956 1.306563
Penalty: 1.278977e-13
Beginning step: 22 , rho = 2097152
Iteration: 1
Worth: 0.4142135
X: 0.5411962 1.306563
Penalty: 0
```

## Conclusion

Summing up, we’ve gotten a primary impression of the effectiveness of `torch`

’s L-BFGS optimizer, particularly when used with Robust-Wolfe line search. In actual fact, in numerical optimization – versus deep studying, the place computational velocity is far more of a problem – there’s infrequently a motive to *not* use L-BFGS with line search.

We’ve then caught a glimpse of tips on how to do constrained optimization, a job that arises in lots of real-world purposes. In that regard, this publish feels much more like a starting than a stock-taking. There’s a lot to discover, from normal technique match – when is L-BFGS nicely suited to an issue? – by way of computational efficacy to applicability to totally different species of neural networks. Evidently, if this conjures up you to run your personal experiments, and/or should you use L-BFGS in your personal initiatives, we’d love to listen to your suggestions!

Thanks for studying!

## Appendix

### Rosenbrock operate plotting code

```
library(tidyverse)
a <- 1
b <- 5
rosenbrock <- operate(x) {
x1 <- x[1]
x2 <- x[2]
(a - x1)^2 + b * (x2 - x1^2)^2
}
df <- expand_grid(x1 = seq(-2, 2, by = 0.01), x2 = seq(-2, 2, by = 0.01)) %>%
rowwise() %>%
mutate(x3 = rosenbrock(c(x1, x2))) %>%
ungroup()
ggplot(knowledge = df,
aes(x = x1,
y = x2,
z = x3)) +
geom_contour_filled(breaks = as.numeric(torch_logspace(-3, 3, steps = 50)),
present.legend = FALSE) +
theme_minimal() +
scale_fill_viridis_d(path = -1) +
theme(side.ratio = 1)
```

### Flower operate plotting code

```
a <- 1
b <- 1
c <- 4
flower <- operate(x) {
a * torch_norm(x) + b * torch_sin(c * torch_atan2(x[2], x[1]))
}
df <- expand_grid(x = seq(-3, 3, by = 0.05), y = seq(-3, 3, by = 0.05)) %>%
rowwise() %>%
mutate(z = flower(torch_tensor(c(x, y))) %>% as.numeric()) %>%
ungroup()
ggplot(knowledge = df,
aes(x = x,
y = y,
z = z)) +
geom_contour_filled(present.legend = FALSE) +
theme_minimal() +
scale_fill_viridis_d(path = -1) +
theme(side.ratio = 1)
```

Picture by Michael Trimble on Unsplash

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