#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Synthetic functions for optimization benchmarks.
Reference: https://www.sfu.ca/~ssurjano/optimization.html
"""
from __future__ import annotations
import math
from abc import ABC, abstractmethod
from typing import List, Optional, Tuple
import torch
from torch import Tensor
from torch.nn import Module
[docs]class SyntheticTestFunction(Module, ABC):
r"""Base class for synthetic test functions."""
dim: int
_bounds: List[Tuple[float, float]]
_optimizers: List[Tuple[float, ...]]
_optimal_value: float
_check_grad_at_opt: bool = True
def __init__(self, noise_std: Optional[float] = None, negate: bool = False) -> None:
r"""Base constructor for synthetic test functions.
Arguments:
noise_std: Standard deviation of the observation noise.
negate: If True, negate the function.
"""
super().__init__()
self.noise_std = noise_std
self.negate = negate
self.register_buffer(
"bounds", torch.tensor(self._bounds, dtype=torch.float).transpose(-1, -2)
)
if self._optimizers is not None:
self.register_buffer(
"optimizers", torch.tensor(self._optimizers, dtype=torch.float)
)
@property
def optimal_value(self) -> float:
r"""The global minimum (maximum if negate=True) of the function."""
return -self._optimal_value if self.negate else self._optimal_value
[docs] def forward(self, X: Tensor, noise: bool = True) -> Tensor:
r"""Evaluate the function on a set of points.
Args:
X: The point(s) at which to evaluate the function.
noise: If `True`, add observation noise as specified by `noise_std`.
"""
batch = X.ndimension() > 1
X = X if batch else X.unsqueeze(0)
f = self.evaluate_true(X=X)
if noise and self.noise_std is not None:
f += self.noise_std * torch.randn_like(f)
if self.negate:
f = -f
return f if batch else f.squeeze(0)
[docs] @abstractmethod
def evaluate_true(self, X: Tensor) -> Tensor:
r"""Evaluate the function (w/o observation noise) on a set of points."""
pass # pragma: no cover
[docs]class Ackley(SyntheticTestFunction):
r"""Ackley test function.
d-dimensional function (usually evaluated on `[-32.768, 32.768]^d`):
f(x) = -A exp(-B sqrt(1/d sum_{i=1}^d x_i^2)) -
exp(1/d sum_{i=1}^d cos(c x_i)) + A + exp(1)
f has one minimizer for its global minimum at `z_1 = (0, 0, ..., 0)` with
`f(z_1) = 0`.
"""
_optimal_value = 0.0
_check_grad_at_opt: bool = False
def __init__(
self, dim: int = 2, noise_std: Optional[float] = None, negate: bool = False
) -> None:
self.dim = dim
self._bounds = [(-32.768, 32.768) for _ in range(self.dim)]
self._optimizers = [tuple(0.0 for _ in range(self.dim))]
super().__init__(noise_std=noise_std, negate=negate)
self.a = 20
self.b = 0.2
self.c = 2 * math.pi
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
a, b, c = self.a, self.b, self.c
part1 = -a * torch.exp(-b / math.sqrt(self.dim) * torch.norm(X, dim=-1))
part2 = -torch.exp(torch.mean(torch.cos(c * X), dim=-1))
return part1 + part2 + a + math.e
[docs]class Beale(SyntheticTestFunction):
dim = 2
_optimal_value = 0.0
_bounds = [(-4.5, 4.5), (-4.5, 4.5)]
_optimizers = [(3.0, 0.5)]
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
x1, x2 = X[..., 0], X[..., 1]
part1 = (1.5 - x1 + x1 * x2) ** 2
part2 = (2.25 - x1 + x1 * x2 ** 2) ** 2
part3 = (2.625 - x1 + x1 * x2 ** 3) ** 2
return part1 + part2 + part3
[docs]class Branin(SyntheticTestFunction):
r"""Branin test function.
Two-dimensional function (usually evaluated on `[-5, 10] x [0, 15]`):
B(x) = (x_2 - b x_1^2 + c x_1 - r)^2 + 10 (1-t) cos(x_1) + 10
Here `b`, `c`, `r` and `t` are constants where `b = 5.1 / (4 * math.pi ** 2)`
`c = 5 / math.pi`, `r = 6`, `t = 1 / (8 * math.pi)`
B has 3 minimizers for its global minimum at `z_1 = (-pi, 12.275)`,
`z_2 = (pi, 2.275)`, `z_3 = (9.42478, 2.475)` with `B(z_i) = 0.397887`.
"""
dim = 2
_bounds = [(-5.0, 10.0), (0.0, 15.0)]
_optimal_value = 0.397887
_optimizers = [(-math.pi, 12.275), (math.pi, 2.275), (9.42478, 2.475)]
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
t1 = (
X[..., 1]
- 5.1 / (4 * math.pi ** 2) * X[..., 0] ** 2
+ 5 / math.pi * X[..., 0]
- 6
)
t2 = 10 * (1 - 1 / (8 * math.pi)) * torch.cos(X[..., 0])
return t1 ** 2 + t2 + 10
[docs]class Bukin(SyntheticTestFunction):
dim = 2
_bounds = [(-15.0, -5.0), (-3.0, 3.0)]
_optimal_value = 0.0
_optimizers = [(-10.0, 1.0)]
_check_grad_at_opt: bool = False
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
part1 = 100.0 * torch.sqrt(torch.abs(X[..., 1] - 0.01 * X[..., 0] ** 2))
part2 = 0.01 * torch.abs(X[..., 0] + 10.0)
return part1 + part2
[docs]class Cosine8(SyntheticTestFunction):
r"""Cosine Mixture test function.
8-dimensional function (usually evaluated on `[-1, 1]^8`):
f(x) = 0.1 sum_{i=1}^8 cos(5 pi x_i) - sum_{i=1}^8 x_i^2
f has one maximizer for its global maximum at `z_1 = (0, 0, ..., 0)` with
`f(z_1) = 0.8`
"""
dim = 8
_bounds = [(-1.0, 1.0) for _ in range(8)]
_optimal_value = 0.8
_optimizers = [tuple(0.0 for _ in range(8))]
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
return torch.sum(0.1 * torch.cos(5 * math.pi * X) - X ** 2, dim=-1)
[docs]class DropWave(SyntheticTestFunction):
dim = 2
_bounds = [(-5.12, 5.12), (-5.12, 5.12)]
_optimal_value = -1.0
_optimizers = [(0.0, 0.0)]
_check_grad_at_opt = False
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
norm = torch.norm(X, dim=-1)
part1 = 1.0 + torch.cos(12.0 * norm)
part2 = 0.5 * norm.pow(2) + 2.0
return -part1 / part2
[docs]class DixonPrice(SyntheticTestFunction):
_optimal_value = 0.0
def __init__(
self, dim=2, noise_std: Optional[float] = None, negate: bool = False
) -> None:
self.dim = dim
self._bounds = [(-10.0, 10.0) for _ in range(self.dim)]
self._optimizers = [
tuple(
math.pow(2.0, -(1.0 - 2.0 ** (-(i - 1))))
for i in range(1, self.dim + 1)
)
]
super().__init__(noise_std=noise_std, negate=negate)
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
d = self.dim
part1 = (X[..., 0] - 1) ** 2
i = X.new(range(2, d + 1))
part2 = torch.sum(i * (2.0 * X[..., 1:] ** 2 - X[..., :-1]) ** 2, dim=1)
return part1 + part2
[docs]class EggHolder(SyntheticTestFunction):
r"""Eggholder test function.
Two-dimensional function (usually evaluated on `[-512, 512]^2`):
E(x) = (x_2 + 47) sin(R1(x)) - x_1 * sin(R2(x))
where `R1(x) = sqrt(|x_2 + x_1 / 2 + 47|)`, `R2(x) = sqrt|x_1 - (x_2 + 47)|)`.
"""
dim = 2
_bounds = [(-512.0, 512.0), (-512.0, 512.0)]
_optimal_value = -959.6407
_optimizers = [(512.0, 404.2319)]
_check_grad_at_opt: bool = False
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
x1, x2 = X[..., 0], X[..., 1]
part1 = -(x2 + 47.0) * torch.sin(torch.sqrt(torch.abs(x2 + x1 / 2.0 + 47.0)))
part2 = -x1 * torch.sin(torch.sqrt(torch.abs(x1 - (x2 + 47.0))))
return part1 + part2
[docs]class Griewank(SyntheticTestFunction):
_optimal_value = 0.0
def __init__(
self, dim=2, noise_std: Optional[float] = None, negate: bool = False
) -> None:
self.dim = dim
self._bounds = [(-600.0, 600.0) for _ in range(self.dim)]
self._optimizers = [tuple(0.0 for _ in range(self.dim))]
super().__init__(noise_std=noise_std, negate=negate)
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
part1 = torch.sum(X ** 2 / 4000.0, dim=1)
d = X.shape[1]
part2 = -torch.prod(
torch.cos(X / torch.sqrt(X.new(range(1, d + 1))).view(1, -1))
)
return part1 + part2 + 1.0
[docs]class Hartmann(SyntheticTestFunction):
r"""Hartmann synthetic test function.
Most commonly used is the six-dimensional version (typically evaluated on
`[0, 1]^6`):
H(x) = - sum_{i=1}^4 ALPHA_i exp( - sum_{j=1}^6 A_ij (x_j - P_ij)**2 )
H has a 6 local minima and a global minimum at
z = (0.20169, 0.150011, 0.476874, 0.275332, 0.311652, 0.6573)
with `H(z) = -3.32237`.
"""
def __init__(
self, dim=6, noise_std: Optional[float] = None, negate: bool = False
) -> None:
if dim not in (3, 4, 6):
raise ValueError(f"Hartmann with dim {dim} not defined")
self.dim = dim
self._bounds = [(0.0, 1.0) for _ in range(self.dim)]
# optimizers and optimal values for dim=4 not implemented
optvals = {3: -3.86278, 6: -3.32237}
optimizers = {
3: [(0.114614, 0.555649, 0.852547)],
6: [(0.20169, 0.150011, 0.476874, 0.275332, 0.311652, 0.6573)],
}
self._optimal_value = optvals.get(self.dim)
self._optimizers = optimizers.get(self.dim)
super().__init__(noise_std=noise_std, negate=negate)
self.register_buffer("ALPHA", torch.tensor([1.0, 1.2, 3.0, 3.2]))
if dim == 3:
A = [[3.0, 10, 30], [0.1, 10, 35], [3.0, 10, 30], [0.1, 10, 35]]
P = [
[3689, 1170, 2673],
[4699, 4387, 7470],
[1091, 8732, 5547],
[381, 5743, 8828],
]
elif dim == 4:
A = [
[10, 3, 17, 3.5],
[0.05, 10, 17, 0.1],
[3, 3.5, 1.7, 10],
[17, 8, 0.05, 10],
]
P = [
[1312, 1696, 5569, 124],
[2329, 4135, 8307, 3736],
[2348, 1451, 3522, 2883],
[4047, 8828, 8732, 5743],
]
elif dim == 6:
A = [
[10, 3, 17, 3.5, 1.7, 8],
[0.05, 10, 17, 0.1, 8, 14],
[3, 3.5, 1.7, 10, 17, 8],
[17, 8, 0.05, 10, 0.1, 14],
]
P = [
[1312, 1696, 5569, 124, 8283, 5886],
[2329, 4135, 8307, 3736, 1004, 9991],
[2348, 1451, 3522, 2883, 3047, 6650],
[4047, 8828, 8732, 5743, 1091, 381],
]
self.register_buffer("A", torch.tensor(A, dtype=torch.float))
self.register_buffer("P", torch.tensor(P, dtype=torch.float))
@property
def optimal_value(self) -> float:
if self.dim == 4:
raise NotImplementedError()
return super().optimal_value
@property
def optimizers(self) -> Tensor:
if self.dim == 4:
raise NotImplementedError()
return super().optimizers
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
self.to(device=X.device, dtype=X.dtype)
inner_sum = torch.sum(self.A * (X.unsqueeze(1) - 0.0001 * self.P) ** 2, dim=2)
H = -torch.sum(self.ALPHA * torch.exp(-inner_sum), dim=1)
if self.dim == 4:
H = (1.1 + H) / 0.839
return H
[docs]class HolderTable(SyntheticTestFunction):
r"""Holder Table synthetic test function.
Two-dimensional function (typically evaluated on `[0, 10] x [0, 10]`):
`H(x) = - | sin(x_1) * cos(x_2) * exp(| 1 - ||x|| / pi | ) |`
H has 4 global minima with `H(z_i) = -19.2085` at
z_1 = ( 8.05502, 9.66459)
z_2 = (-8.05502, -9.66459)
z_3 = (-8.05502, 9.66459)
z_4 = ( 8.05502, -9.66459)
"""
dim = 2
_bounds = [(-10.0, 10.0)]
_optimal_value = -19.2085
_optimizers = [
(8.05502, 9.66459),
(-8.05502, -9.66459),
(-8.05502, 9.66459),
(8.05502, -9.66459),
]
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
term = torch.abs(1 - torch.norm(X, dim=1) / math.pi)
return -torch.abs(torch.sin(X[..., 0]) * torch.cos(X[..., 1]) * torch.exp(term))
[docs]class Levy(SyntheticTestFunction):
r"""Levy synthetic test function.
d-dimensional function (usually evaluated on `[-10, 10]^d`):
f(x) = sin^2(pi w_1) +
sum_{i=1}^{d-1} (w_i-1)^2 (1 + 10 sin^2(pi w_i + 1)) +
(w_d - 1)^2 (1 + sin^2(2 pi w_d))
where `w_i = 1 + (x_i - 1) / 4` for all `i`.
f has one minimizer for its global minimum at `z_1 = (1, 1, ..., 1)` with
`f(z_1) = 0`.
"""
_optimal_value = 0.0
def __init__(
self, dim=2, noise_std: Optional[float] = None, negate: bool = False
) -> None:
self.dim = dim
self._bounds = [(-10.0, 10.0) for _ in range(self.dim)]
self._optimizers = [tuple(1.0 for _ in range(self.dim))]
super().__init__(noise_std=noise_std, negate=negate)
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
w = 1.0 + (X - 1.0) / 4.0
part1 = torch.sin(math.pi * w[..., 0]) ** 2
part2 = torch.sum(
(w[..., :-1] - 1.0) ** 2
* (1.0 + 10.0 * torch.sin(math.pi * w[..., :-1] + 1.0) ** 2),
dim=1,
)
part3 = (w[..., -1] - 1.0) ** 2 * (
1.0 + torch.sin(2.0 * math.pi * w[..., -1]) ** 2
)
return part1 + part2 + part3
[docs]class Michalewicz(SyntheticTestFunction):
r"""Michalewicz synthetic test function.
d-dim function (usually evaluated on hypercube [0, pi]^d):
M(x) = sum_{i=1}^d sin(x_i) (sin(i x_i^2 / pi)^20)
"""
def __init__(
self, dim=2, noise_std: Optional[float] = None, negate: bool = False
) -> None:
self.dim = dim
self._bounds = [(0.0, math.pi) for _ in range(self.dim)]
optvals = {2: -1.80130341, 5: -4.687658, 10: -9.66015}
optimizers = {2: [(2.20290552, 1.57079633)]}
self._optimal_value = optvals.get(self.dim)
self._optimizers = optimizers.get(self.dim)
super().__init__(noise_std=noise_std, negate=negate)
self.register_buffer(
"i", torch.tensor(tuple(range(1, self.dim + 1)), dtype=torch.float)
)
@property
def optimizers(self) -> Tensor:
if self.dim in (5, 10):
raise NotImplementedError()
return super().optimizers
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
self.to(device=X.device, dtype=X.dtype)
m = 10
return -torch.sum(
torch.sin(X) * torch.sin(self.i * X ** 2 / math.pi) ** (2 * m), dim=-1
)
[docs]class Powell(SyntheticTestFunction):
_optimal_value = 0.0
def __init__(
self, dim=4, noise_std: Optional[float] = None, negate: bool = False
) -> None:
self.dim = dim
self._bounds = [(-4.0, 5.0) for _ in range(self.dim)]
self._optimizers = [tuple(0.0 for _ in range(self.dim))]
super().__init__(noise_std=noise_std, negate=negate)
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
result = torch.zeros_like(X[..., 0])
for i in range(self.dim // 4):
i_ = i + 1
part1 = (X[..., 4 * i_ - 4] + 10.0 * X[..., 4 * i_ - 3]) ** 2
part2 = 5.0 * (X[..., 4 * i_ - 2] - X[..., 4 * i_ - 1]) ** 2
part3 = (X[..., 4 * i_ - 3] - 2.0 * X[..., 4 * i_ - 2]) ** 4
part4 = 10.0 * (X[..., 4 * i_ - 4] - X[..., 4 * i_ - 1]) ** 4
result += part1 + part2 + part3 + part4
return result
[docs]class Rastrigin(SyntheticTestFunction):
_optimal_value = 0.0
def __init__(
self, dim=2, noise_std: Optional[float] = None, negate: bool = False
) -> None:
self.dim = dim
self._bounds = [(-5.12, 5.12) for _ in range(self.dim)]
self._optimizers = [tuple(0.0 for _ in range(self.dim))]
super().__init__(noise_std=noise_std, negate=negate)
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
return 10.0 * self.dim + torch.sum(
X ** 2 - 10.0 * torch.cos(2.0 * math.pi * X), dim=-1
)
[docs]class Rosenbrock(SyntheticTestFunction):
r"""Rosenbrock synthetic test function.
d-dimensional function (usually evaluated on `[-5, 10]^d`):
f(x) = sum_{i=1}^{d-1} (100 (x_{i+1} - x_i^2)^2 + (x_i - 1)^2)
f has one minimizer for its global minimum at `z_1 = (1, 1, ..., 1)` with
`f(z_i) = 0.0`.
"""
_optimal_value = 0.0
def __init__(
self, dim=2, noise_std: Optional[float] = None, negate: bool = False
) -> None:
self.dim = dim
self._bounds = [(-5.0, 10.0) for _ in range(self.dim)]
self._optimizers = [tuple(1.0 for _ in range(self.dim))]
super().__init__(noise_std=noise_std, negate=negate)
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
return torch.sum(
100.0 * (X[..., 1:] - X[..., :-1] ** 2) ** 2 + (X[..., :-1] - 1) ** 2,
dim=-1,
)
[docs]class Shekel(SyntheticTestFunction):
r"""Shekel synthtetic test function.
4-dimensional function (usually evaluated on `[0, 10]^4`):
f(x) = -sum_{i=1}^10 (sum_{j=1}^4 (x_j - A_{ji})^2 + C_i)^{-1}
f has one minimizer for its global minimum at `z_1 = (4, 4, 4, 4)` with
`f(z_1) = -10.5363`.
"""
dim = 4
_bounds = [(0.0, 10.0), (0.0, 10.0), (0.0, 10.0), (0.0, 10.0)]
_optimizers = [(4.000747, 3.99951, 4.00075, 3.99951)]
def __init__(
self, m: int = 10, noise_std: Optional[float] = None, negate: bool = False
) -> None:
self.m = m
optvals = {5: -10.1532, 7: -10.4029, 10: -10.536443}
self._optimal_value = optvals[self.m]
super().__init__(noise_std=noise_std, negate=negate)
self.register_buffer(
"beta", torch.tensor([1, 2, 2, 4, 4, 6, 3, 7, 5, 5], dtype=torch.float)
)
C_t = torch.tensor(
[
[4, 1, 8, 6, 3, 2, 5, 8, 6, 7],
[4, 1, 8, 6, 7, 9, 3, 1, 2, 3.6],
[4, 1, 8, 6, 3, 2, 5, 8, 6, 7],
[4, 1, 8, 6, 7, 9, 3, 1, 2, 3.6],
],
dtype=torch.float,
)
self.register_buffer("C", C_t.transpose(-1, -2))
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
self.to(device=X.device, dtype=X.dtype)
beta = self.beta / 10.0
result = -sum(
1 / (torch.sum((X - self.C[i]) ** 2, dim=-1) + beta[i])
for i in range(self.m)
)
return result
[docs]class SixHumpCamel(SyntheticTestFunction):
dim = 2
_bounds = [(-3.0, 3.0), (-2.0, 2.0)]
_optimal_value = -1.0316
_optimizers = [(0.0898, -0.7126), (-0.0898, 0.7126)]
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
x1, x2 = X[..., 0], X[..., 1]
return (
(4 - 2.1 * x1 ** 2 + x1 ** 4 / 3) * x1 ** 2
+ x1 * x2
+ (4 * x2 ** 2 - 4) * x2 ** 2
)
[docs]class StyblinskiTang(SyntheticTestFunction):
r"""Styblinski-Tang synthtetic test function.
d-dimensional function (usually evaluated on the hypercube `[-5, 5]^d`):
H(x) = 0.5 * sum_{i=1}^d (x_i^4 - 16 * x_i^2 + 5 * x_i)
H has a single global mininimum `H(z) = -39.166166 * d` at `z = [-2.903534]^d`
"""
def __init__(
self, dim=2, noise_std: Optional[float] = None, negate: bool = False
) -> None:
self.dim = dim
self._bounds = [(-5.0, 5.0) for _ in range(self.dim)]
self._optimal_value = -39.166166 * self.dim
self._optimizers = [tuple(-2.903534 for _ in range(self.dim))]
super().__init__(noise_std=noise_std, negate=negate)
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
return 0.5 * (X ** 4 - 16 * X ** 2 + 5 * X).sum(dim=1)
[docs]class ThreeHumpCamel(SyntheticTestFunction):
dim = 2
_bounds = [(-5.0, 5.0), (-5.0, 5.0)]
_optimal_value = 0.0
_optimizers = [(0.0, 0.0)]
[docs] def evaluate_true(self, X: Tensor) -> Tensor:
x1, x2 = X[..., 0], X[..., 1]
return 2.0 * x1 ** 2 - 1.05 * x1 ** 4 + x1 ** 6 / 6.0 + x1 * x2 + x2 ** 2
