# Posit AI Weblog: torch for optimization

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To this point, all `torch` use instances we’ve mentioned right here have been in deep studying. Nevertheless, its computerized differentiation function is beneficial in different areas. One outstanding instance is numerical optimization: We are able to use `torch` to seek out the minimal of a operate.

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

This put up 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 impression of operating L-BFGS with line search. As a enjoyable add-on, we present an instance of constrained optimization, the place a constraint is enforced through a quadratic penalty operate.

To heat up, we take a detour, minimizing a operate “ourselves” utilizing nothing however tensors. This may transform related later, although, as the general course of will nonetheless be the identical. All modifications shall be associated to integration of `optimizer`s and their capabilities.

## Operate minimization, DYI strategy

To see how we are able to decrease a operate “by hand”, let’s attempt the long-lasting Rosenbrock operate. It is 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 usually set to 1 and 5, respectively.

In R:

``````library(torch)

a <- 1
b <- 5

rosenbrock <- operate(x) {
x1 <- x[1]
x2 <- x[2]
(a - x1)^2 + b * (x2 - x1^2)^2
}``````

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

Our purpose and technique are as follows.

We need to discover the values (x_1) and (x_2) for which the operate attains its minimal. We’ve got to start out someplace; and from wherever that will get us on the graph we observe 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 beneath a selected threshold.

Right here is the corresponding code. For no particular causes, we begin at `(-1,1)` . The training price (the fraction of the gradient to subtract) wants some experimentation. (Strive 0.1 and 0.001 to see its impression.)

``````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()

# guide replace
})
}``````
``````Iteration:  100
Worth is:  0.3502924

Iteration:  200
Worth is:  0.07398106

...
...

Iteration:  900
Worth is:  0.0001532408

Iteration:  1000
Worth is:  6.962555e-05

Whereas this works, it actually serves as an instance the precept. With `torch` offering a bunch of confirmed optimization algorithms, there isn’t any 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.

With Adam, optimization proceeds so much sooner. Fact be advised, although, selecting a superb studying price nonetheless takes non-negligeable experimentation. (Strive the default studying price, 0.001, for comparability.)

``````num_iterations <- 100

x_star <- torch_tensor(c(-1, 1), requires_grad = TRUE)

lr <- 1

for (i in 1:num_iterations) {

if (i %% 10 == 0) cat("Iteration: ", i, "n")

worth <- rosenbrock(x_star)
if (i %% 10 == 0) cat("Worth is: ", as.numeric(worth), "n")

worth\$backward()
optimizer\$step()

}``````
``````Iteration:  10
Worth is:  0.8559565

Iteration:  20
Worth is:  0.1282992

...
...

Iteration:  90
Worth is:  4.003079e-05

Iteration:  100
Worth is:  6.937736e-05

It took us a couple of hundred iterations to reach at a good worth. It is a lot sooner than the guide strategy above, however nonetheless rather a lot. Fortunately, additional enhancements are potential.

### L-BFGS

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

BFGS is maybe probably 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 data solely, second-order algorithms moreover take curvature data 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.

Trying on the contours of the Rosenbrock operate, with its extended, slender valley, it isn’t tough to think about that curvature data 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 beneath snippet), for them to be callable a number of instances per iteration. You may 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() {

worth <- rosenbrock(x_star)
cat("Worth is: ", as.numeric(worth), "n")

worth\$backward()
worth

}

for (i in 1:num_iterations) {
cat("Iteration: ", i, "n")
optimizer\$step(calc_loss)
}``````
``````Iteration:  1
Worth is:  4

Worth is:  6

...
...

Worth is:  0.04880721

Worth is:  0.0302862

Iteration:  2
Worth is:  0.01697086

Worth is:  0.01124081

...
...

Worth is:  1.111701e-09

Worth is:  4.547474e-12

Iteration:  3
Worth is:  4.547474e-12

Regardless that we ran the algorithm for 3 iterations, the optimum worth actually is reached after two. Seeing how properly this labored, we attempt L-BFGS on a tougher 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)`, because the `atan2` used within the operate just isn’t 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() {

worth <- flower(x_star)
cat("Worth is: ", as.numeric(worth), "n")

worth\$backward()

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
X is:  20 20

...
...

Worth is:  19.33546
X is:  12.957 14.68274

...
...

Worth is:  18.29546
X is:  12.14691 14.06392

...
...

Worth is:  9.853705
X is:  5.763702 8.895616

Worth is:  2635.866
X is:  -1949.697 -1773.551

Iteration:  2
Worth is:  1333.113
X is:  -985.4553 -897.5367

Worth is:  30.16862
X is:  -21.02814 -21.72296

Worth is:  1281.39
X is:  964.0121 843.7817

Worth is:  628.1306
X is:  475.7051 409.7372

Worth is:  4965690
X is:  -3721262 -3287901

Worth is:  2482306
X is:  -1862675 -1640817

Worth is:  8.61863e+11
X is:  645200412672 571423064064

Worth is:  430929412096
X is:  322643460096 285659529216

Worth is:  Inf
X is:  -2.826342e+19 -2.503904e+19

Iteration:  3
Worth is:  Inf
X is:  -2.826342e+19 -2.503904e+19 ``````

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

Fortunately, there’s something we are able to do.

Taken in isolation, what a Quasi-Newton methodology like L-BFGS does is decide the perfect descent path. Nevertheless, as we simply noticed, a superb path just isn’t 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 fastidiously 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 Sturdy Wolfe situations. We re-run the above code, altering simply two traces. 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
X is:  0.09035123 -0.03220009

Worth is:  -0.928809
X is:  0.06564617 -0.026706

Iteration:  2
...
...

Worth is:  -0.9991404
X is:  0.0006493925 -0.0002656128

Worth is:  -0.9992246
X is:  0.0007130796 -0.0002947929

Iteration:  3
...
...

Worth is:  -0.9997789
X is:  0.0002042478 -8.457939e-05

Worth is:  -0.9998025
X is:  0.0001822711 -7.553725e-05

Iteration:  4
...
...

Worth is:  -0.9999917
X is:  -6.320081e-06 2.614706e-06

Worth is:  -0.9999923
X is:  -6.921942e-06 2.865841e-06

Iteration:  5
...
...

Worth is:  -0.9999999
X is:  -7.267168e-08 3.009783e-08

Worth is:  -0.9999999
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 meet some variety of further situations. In optimization lingo, it needs to be possible.

As an instance, 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 decrease 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, leads to 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))

penalty <- operate(x) torch_square(torch_max(constraint(x), different = 0))``````

A priori, we are able to’t understand how massive that a number of needs to be to implement the constraint. Due to this fact, optimization proceeds iteratively. We begin with a small multiplier, (1), say, and improve it for so long as the constraint continues to be violated:

``````penalty_method <- operate(f, p, x, k_max, rho = 1, gamma = 2, num_iterations = 1) {

for (ok in 1:k_max) {
cat("Beginning step: ", ok, ", rho = ", rho, "n")

decrease(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
}

}``````

`decrease()`, referred to as from `penalty_method()`, follows the standard proceedings, however now it minimizes the sum of the goal and up-weighted penalty operate outputs:

``````decrease <- operate(f, p, x, rho, num_iterations) {

calc_loss <- operate() {
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 Sturdy-Wolfe line search. In reality, in numerical optimization – versus deep studying, the place computational velocity is rather more of a problem – there’s hardly a cause to not use L-BFGS with line search.

We’ve then caught a glimpse of easy methods to do constrained optimization, a process that arises in lots of real-world purposes. In that regard, this put up feels much more like a starting than a stock-taking. There’s a lot to discover, from normal methodology match – when is L-BFGS properly suited to an issue? – through computational efficacy to applicability to totally different species of neural networks. For sure, if this conjures up you to run your individual experiments, and/or if you happen to use L-BFGS in your individual 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(information = 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(information = 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