Consider the Traveling Salesperson Problem:
Given a set of cities and the distances between each pair of cities, what is the shortest possible tour that visits each city exactly once, and returns to the starting city?
In this notebook we will develop some solutions to the problem, and more generally show how to think about solving a problem like this.
Do we understand precisely what the problem is asking? Do we understand all the concepts that the problem talks about? Do we understand them well enough to implement them in a programming language? Let's take a first pass:
set
datatype might be appropriate.A
and B
are cities, this could be a function, distance(A, B),
or a table lookup, distance[A][B]
. The resulting distance will be a real number.list
or tuple
datatypes would work. For example, given the set of cities {A, B, C, D}
, a tour might be the list [B, D, A, C]
, which means to travel from B
to D
to A
to C
and finally back to B
.tour_length(tour)
.tsp
", the traditional abbreviation for Traveling Salesperson Problem.At this stage I have a rough sketch of how to attack the problem. I don't have all the answers, and I haven't committed to specific representations for all the concepts, but I know what all the pieces are, and I don't see anything that stops me from proceeding.
This document is an iPython notebook: a mix of text and Python code that you can run. You can learn more from the "Help" button at the top of the page. But first we'll take care of some preliminaries: the Python statements that import standard libraries that we will be using later on:
%pylab --no-import-all inline
from __future__ import division
import matplotlib.pyplot as plt
import random
import time
import itertools
import urllib
import csv
alltours_tsp
)¶Let's start with an algorithm that is guaranteed to solve the problem (although it is inefficient for large sets of cities). Here is a sketch of the algorithm:
All Tours Algorithm: Generate all possible tours of the cities, and choose the shortest tour (the one with minimum tour length).
In general our design philosophy is to first write an English description of the algorithm, then write Python code that closely mirrors the English description. This will probably require some auxilliary functions and data structures; just assume they exist; put them on a TO DO list, and eventually define them with the same design philosophy.
Here is the start of the implementation:
def alltours_tsp(cities):
"Generate all possible tours of the cities and choose the shortest tour."
return shortest_tour(alltours(cities))
def shortest_tour(tours):
"Choose the tour with the minimum tour length."
return min(tours, key=tour_length)
# TO DO: Data types: cities, tours, Functions: alltours, tour_length
Note: In Python min(
collection,key=
function)
means to find the element x that is a member of collection such that function(x) is minimized. So shortest
finds the tour whose tour_length
in the minimal among the tours.
This gives us a good start; the Python code closely matches the English description. And we know what we need to do next: represent cities and tours, and implement the functions alltours
and tour_length
. Let's start with tours.
A tour starts in one city, and then visits each of the other cities in order, before returning to the start city. A natural representation of a tour is a sequence of cities. For example (1, 2, 3)
could represent a tour that starts in city 1, moves to 2, then 3, and finally returns to 1.
Note: I considered using (1, 2, 3, 1)
as the representation of this tour. I also considered an ordered list of edges between cities:
((1, 2), (2, 3), (3, 1))
. In the end, I decided (1, 2, 3)
was simplest.
Now for the alltours
function. If a tour is a sequence of cities, then all the tours are permutations of the set of all cities. A function to generate all permutations of a set is already provided in Python's standard itertools
library module; we can use it as our implementation of alltours
:
alltours = itertools.permutations
For n cities there are n! (that is, the factorial of n) permutations. Here's are all 3! = 6 tours of 3 cities:
cities = {1, 2, 3}
list(alltours(cities))
The length of a tour is the sum of the lengths of each edge in the tour; in other words, the sum of the distances between consecutive cities in the tour, including the distance form the last city back to the first:
def tour_length(tour):
"The total of distances between each pair of consecutive cities in the tour."
return sum(distance(tour[i], tour[i-1])
for i in range(len(tour)))
# TO DO: Functions: distance, Data types: cities
Note: I use one Python-specific trick: when i
is 0, then distance(tour[0], tour[-1])
gives us the wrap-around distance between the first and last cities, because tour[-1]
is the last element of tour
.
We determined that the only thing that matters about cities is the distance between them. But before we can decide about how to represent cities, and before we can define distance(A, B)
, we have to make a choice. In the fully general version of the TSP, the "distance" between two cities could be anything: it could factor in the amount of time it takes to travel between cities, the twistiness of the road, or anything else. The distance(A, B)
might be different from distance(B, A)
. So the distances could be represented by a matrix distance[A][B]
, where any entry in the matrix could be any (non-negative) numeric value.
But we will ignore the fully general TSP and concentrate on an important special case, the Euclidean TSP, where the distance between any two cities is the Euclidean distance, the straight-line distance between points in a two-dimensional plane. So a city can be represented by a two-dimensional point: a pair of x and y coordinates. We will use the constructor function City
, so that City(300, 0)
creates a city with x-coordinate of 300 and y coordinate of 0. Then distance(A, B)
will be a function that uses the x and y coordinates to compute the distance between A
and B
.
distance
¶OK, so a city can be represented as just a two-dimensional point. But how will we represent points? Here are some choices, with their pros and cons:
tuple: A point is a two-tuple of (x, y) coordinates, for example, (300, 0)
. Pro: Very simple.
Con: doesn't distinguish Points from other two-tuples.
class: Define a custom Point
class with x and y slots. Pro: explicit, gives us p.x
and p.y
accessors. Con: less efficient.
complex: Python already has the two-dimensional point as a built-in numeric data type, but in a non-obvious way: as complex
numbers, which inhabit the two-dimensional (real × imaginary) plane. Pro: efficient. Con: a little confusing; doesn't distinguish Points from other complex numbers.
Any of these choices would work perfectly well; I decided to use complex numbers and to implement the functions X
and Y
to make things less confusing. An advantage of complex
numbers is that computing the distance between two points is easy—the absoute value of their difference:
# Cities are represented as Points, which are represented as complex numbers
Point = complex
City = Point
def X(point):
"The x coordinate of a point."
return point.real
def Y(point):
"The y coordinate of a point."
return point.imag
def distance(A, B):
"The distance between two points."
return abs(A - B)
Here's an example of computing the distance between two cities:
A = City(3, 0)
B = City(0, 4)
distance(A, B)
The input to a TSP algorithm should be a set of cities. I can make a random set of n cities by calling City
n times, each with different random x and y coordinates:
{City(random.randrange(1000), random.randrange(1000)) for c in range(6)}
The function Cities
does that (and a bit more):
def Cities(n, width=900, height=600, seed=42):
"Make a set of n cities, each with random coordinates within a (width x height) rectangle."
random.seed(seed * n)
return frozenset(City(random.randrange(width), random.randrange(height))
for c in range(n))
There are three complications that I decided to tackle in Cities
:
Complication 1: IPython's matplotlib plots (by default) in a rectangle that is 1.5 times wider than it is high; that's why I specified a width of 900 and a height of 600. If you want the coordinates of your cities to be bounded by a different size rectangle, you can change width or height.
Complication 2: Sometimes I want Cities(n)
to be a true function, returning the same result each time. This is very helpful for getting repeatable results: if I run a test twice, I get the same results twice.
But other times I would like to be able to do an experiment, where, for example, I call Cities(n)
30 times and get 30 different sets, and I then compute the average tour length produced by my algorithm across these 30 sets. Can I get both behaviors out of one function? Yes! The trick is the additional optional parameter, seed
. Two calls to Cities
with the same n
and seed
parameters will always return the same set of cities, and two calls with different values for seed
will return different sets. This is implemented by calling the function random.seed
, which resets the random number generator.
Complication 3: Once I create a set of Cities, I don't want anyone messing with my set. For example, I don't want an algorithm that claims to "solve" a problem by deleting half the cities from the input set, then finding a tour of the remaining cities. Therefore, I make Cities
return a frozenset
rather than a set
. A frozenset
is immutable; nobody can change it once it is created.
For example:
# A set of 5 cities
Cities(5)
# The same set of 5 cities each time
[Cities(5) for i in range(3)]
# A different set of 5 cities each time
[Cities(5, seed=i) for i in range(3)]
Now we are ready to apply the alltours_tsp
function to find the shortest tour:
alltours_tsp(Cities(8))
tour_length(alltours_tsp(Cities(8)))
Quick, is that the right answer? I have no idea, and you probably can't tell either. But if we could plot the tour we'd understand it better and might be able to see at a glance if the tour is optimal.
I define plot_tour(tour)
to plot the cities (as circles) and the tour (as lines):
def plot_tour(tour):
"Plot the cities as circles and the tour as lines between them."
plot_lines(list(tour) + [tour[0]])
def plot_lines(points, style='bo-'):
"Plot lines to connect a series of points."
plt.plot(map(X, points), map(Y, points), style)
plt.axis('scaled'); plt.axis('off')
plot_tour(alltours_tsp(Cities(8)))
That looks much better! To me, it looks like the shortest possible tour, although I don't have an easy way to prove it. Let's go one step further and define a function, plot_tsp(algorithm, cities)
that will take a TSP algorithm (such as alltours_tsp
) and a set of cities, apply the algorithm to the cities to get a tour, check that the tour is reasonable, plot the tour, and print information about the length of the tour and the time it took to find it:
def plot_tsp(algorithm, cities):
"Apply a TSP algorithm to cities, plot the resulting tour, and print information."
# Find the solution and time how long it takes
t0 = time.clock()
tour = algorithm(cities)
t1 = time.clock()
assert valid_tour(tour, cities)
plot_tour(tour); plt.show()
print("{} city tour with length {:.1f} in {:.3f} secs for {}"
.format(len(tour), tour_length(tour), t1 - t0, algorithm.__name__))
def valid_tour(tour, cities):
"Is tour a valid tour for these cities?"
return set(tour) == set(cities) and len(tour) == len(cities)
plot_tsp(alltours_tsp, Cities(8))
alltours_tsp
)¶We said there are n! tours of n cities, and thus 6 tours of 3 cities:
list(alltours({1, 2, 3}))
But this is redundant: (1, 2, 3)
, (2, 3, 1)
, and (3, 1, 2)
are three ways of describing the same tour. So let's arbitrarily say that all tours must start with the first city in the set of cities. We'll just pull the first city out, and then tack it back on to all the permutations of the rest of the cities.
While we're re-assembling a tour from the start city and the rest, we'll take the opportunity to construct the tour as a list rather than a tuple. It doesn't matter much now, but later on we will want to represent partial tours, to which we will want to append cities one by one; appending can only be done to lists, not tuples.
def alltours(cities):
"Return a list of tours, each a permutation of cities, but each one starting with the same city."
start = first(cities)
return [[start] + Tour(rest)
for rest in itertools.permutations(cities - {start})]
def first(collection):
"Start iterating over collection, and return the first element."
return next(iter(collection))
Tour = list # Tours are implemented as lists of cities
We can verify that for 3 cities there are now only 2 tours (not 6) and for 4 cities there are 6 tours (not 24):
alltours({1, 2, 3})
alltours({1, 2, 3, 4})
Note: We could say that there is only one tour of three cities, because [1, 2, 3]
and [1, 3, 2]
are in some sense the same tour, one going clockwise and the other counterclockwise. However, I choose not to do that, for two reasons. First, it would mean we can never handle maps where the distance from A to B is different from B to A. Second, it would complicate the code (if only by a line or two) while not saving much run time.
We can verify that calling alltours_tsp(Cities(8))
still works and gives the same tour with the same total distance. But it now runs faster:
plot_tsp(alltours_tsp, Cities(8))
Now let's try a much harder 10-city tour:
plot_tsp(alltours_tsp, Cities(10))
alltours_tsp
¶It takes about 2 seconds on my machine to solve this 10-city problem. In general, the function TSP
looks at (n-1)! tours for an n-city problem, and each tour has n cities, so the total time required for n cities should be roughly proportional to n!. This means that the time grows rapidly with the number of cities. Really rapidly. This table shows the actual time for solving a 10 city problem, and the exepcted time for solving larger problems:
n | expected time for `alltours_tsp(Cities(n))` |
---|---|
10 | Covering 10! tours = 2 secs |
11 | 2 secs × 11! / 10! ≈ 22 secs |
12 | 2 secs × 12! / 10! ≈ 4 mins |
14 | 2 secs × 14! / 10! ≈ 13 hours |
16 | 2 secs × 16! / 10! ≈ 200 days |
18 | 2 secs × 18! / 10! ≈ 112 years |
25 | 2 secs × 25! / 10! ≈ 270 billion years |
There must be a better way ...
What if we are willing to settle for a tour that is short, but not guaranteed to be shortest? Then we can save billions of years of compute time: we will show several approximate algorithms, which find tours that are typically within 10% of the shortest possible tour, and can handle thousands of cities in a few seconds. (Note: There are more sophisticated approximate algorithms that can handle hundreds of thousands of cities and come within 0.01% or better of the shortest possible tour.)
So how do we come up with an approximate algorithm? Here are two general plans of how to create a tour:
We will expand these ideas into full algorithms.
In addition, here are four very general strategies that apply not just to TSP, but to any optimization problem. An optimization problem is one in which the goal is to find a solution that is best (or near-best) according to some metric, out of a pool of many candidate solutions. The strategies are:
And here are two more strategies that work for a wide variety of problems:
Divide and Conquer: Split the input in half, solve the problem for each half, and then combine the two partial solutions.
Stand on the Shoulders of Giants or Just Google It: Find out what others have done in the past, and either copy it or build on it.
nn_tsp
)¶Here is a description of the nearest neighbor algorithm:
Nearest Neighbor Algorithm: Start at any city; at each step extend the tour by moving from the previous city to its nearest neighbor that has not yet been visited.
Often the best place for a tour to go next is to its nearest neighbor. But sometimes that neighbor would be better visited at some other point in the tour, so this algorithm is not guaranteed to find the shortest tour.
To implement the algorithm I need to represent all the noun phrases in the English description: "any city" (a city; arbitrarily the first city); "the tour" (a list of cities, initialy just the start city); "previous city" (the last element of tour, that is, tour[-1]
); "nearest neighbor" (a function that, when given a city, A, and a list of other cities, finds the one with minimal distance from A); and "not yet been visited" (we will keep a set of unvisited cities; initially all cities but the start city are unvisited).
The algorithm (in more detail) is as follows:
def nn_tsp(cities):
"""Start the tour at the first city; at each step extend the tour
by moving from the previous city to its nearest neighbor
that has not yet been visited."""
start = first(cities)
tour = [start]
unvisited = set(cities - {start})
while unvisited:
C = nearest_neighbor(tour[-1], unvisited)
tour.append(C)
unvisited.remove(C)
return tour
def nearest_neighbor(A, cities):
"Find the city in cities that is nearest to city A."
return min(cities, key=lambda c: distance(c, A))
Note: In Python, as in the formal mathematical theory of computability, lambda
(or λ) is the symbol for function, so "lambda c: distance(c, A)
" means the function of c
that computes the distance from c
to the city A
.
We can compare the fast (but approximate) nn_tsp
algorithm to the slow (but optimal) alltours_tsp
algorithm on a small map:
plot_tsp(alltours_tsp, Cities(9))
plot_tsp(nn_tsp, Cities(9))
So the nearest neighbor approach was a lot faster, but it didn't find the shortest tour. To understand where it went wrong, it would be helpful to know what city it started from. I can modify plot_tour
by adding one line of code to highlight the start city with a red square:
def plot_tour(tour):
"Plot the cities as circles and the tour as lines between them. Start city is red square."
start = tour[0]
plot_lines(list(tour) + [start])
plot_lines([start], 'rs') # Mark the start city with a red square
plot_tsp(nn_tsp, Cities(9))
We can see that the tour moves clockwise from the start city, and mostly makes good decisions, but not optimal ones.
We can compare the performance of these two algorithms on, say, eleven different sets of cities instead of just one:
def length_ratio(cities):
"The ratio of the tour lengths for nn_tsp and alltours_tsp algorithms."
return tour_length(nn_tsp(cities)) / tour_length(alltours_tsp(cities))
sorted(length_ratio(Cities(8, seed=i)) for i in range(11))
The ratio of 1.0
means the two algorithms got the same (optimal) result; that happened 5 times out of 10. The other times, we see that the nn_tsp
produces a longer tour, by anything up to 15% worse, with a median of 0.6% worse.
But more important than that 0.6% (or even 15%) difference is that the nearest neighbor algorithm can quickly tackle problems that the all tours algorithm can't touch in the lifetime of the universe. Finding a tour of 1000 cities takes well under a second:
plot_tsp(nn_tsp, Cities(1000))
Can we do better? Can we combine the speed of the nearest neighbor algorithm with the optimality of the all tours algorithm?
Let's consider what went wrong with nn_tsp
. Looking at plot_tsp(nn_tsp, Cities(100))
, we see that near the end of the tour there are some very long edges between cities, because there are no remaining cities near by. In a way, this just seems like bad luck—the way we flow from neighbor to neighbor just happens to leave a few very-long edges. Just as with buying lottery tickets, we could improve our chance of winning by trying more often; in other words, by using the repetition strategy.
Here is an easy way to apply the repetition strategy to improve nearest neighbors:
Repeated Nearest Neighbor Algorithm: For each of the cities, run the nearest neighbor algorithm with that city as the starting point, and choose the resulting tour with the shortest total distance.
So, with n cities we could run the nn_tsp
algorithm n times, regrettably making the total run time n times longer, but hopefully making at least one of the n tours shorter.
To implement repeated_nn_tsp
we just take the shortest tour over all starting cities:
def repeated_nn_tsp(cities):
"Repeat the nn_tsp algorithm starting from each city; return the shortest tour."
return shortest_tour(nn_tsp(cities, start)
for start in cities)
To do that requires a modification of nn_tsp
so that the start
city can be specified as an optional argument:
def nn_tsp(cities, start=None):
"""Start the tour at the first city; at each step extend the tour
by moving from the previous city to its nearest neighbor
that has not yet been visited."""
if start is None: start = first(cities)
tour = [start]
unvisited = set(cities - {start})
while unvisited:
C = nearest_neighbor(tour[-1], unvisited)
tour.append(C)
unvisited.remove(C)
return tour
# Compare nn_tsp to repeated_nn_tsp
plot_tsp(nn_tsp, Cities(100))
plot_tsp(repeated_nn_tsp, Cities(100))
We see that repeated_nn_tsp
does indeed take longer to run, and yields a tour that is shorter.
Let's try again with a smaller map that makes it easier to visualize the tours:
plot_tsp(nn_tsp, Cities(9))
plot_tsp(repeated_nn_tsp, Cities(9))
plot_tsp(alltours_tsp, Cities(9))
This time the repeated_nn_tsp
gives us a tour that is better tha nn_tsp
, but not quite optimal. So, it looks like repetition is helping. But if I want to tackle 1000 cities, I don't really want the run time to be 1000 times slower. I'd like a way to moderate the repetition—to repeat the nn_tsp
starting from a sample of the cities but not all the cities.
repeated_nn_tsp
)¶We can give repeated_nn_tsp
an optional argument specifying the number of different cities to try starting from. We will implement the function sample
to draw a random sample of the specified size from all the cities. Most of the work is done by the standard library function random.sample
. What our sample
adds is the same thing we did with the function Cities
: we ensure that the function returns the same result each time for the same arguments, but can return different results if a seed
parameter is passed in. (In addition, if the sample size, k
is None
or is larger than the population, then return the whole population.)
def repeated_nn_tsp(cities, repetitions=100):
"Repeat the nn_tsp algorithm starting from specified number of cities; return the shortest tour."
return shortest_tour(nn_tsp(cities, start)
for start in sample(cities, repetitions))
def sample(population, k, seed=42):
"Return a list of k elements sampled from population. Set random.seed with seed."
if k is None or k > len(population):
return population
random.seed(len(population) * k * seed)
return random.sample(population, k)
Let's compare with 1, 10, and 100 starting cities on a 300 city map:
def repeat_10_nn_tsp(cities): return repeated_nn_tsp(cities, 10)
def repeat_100_nn_tsp(cities): return repeated_nn_tsp(cities, 100)
plot_tsp(nn_tsp, Cities(300))
plot_tsp(repeat_10_nn_tsp, Cities(300))
plot_tsp(repeat_100_nn_tsp, Cities(300))
As we add more starting cities, the run times get longer and the tours get shorter.
I'd like to understand the tradefoff better. I'd like to have a way to compare different algorithms (or different choices of parameters for one algorithm) over multiple trials, and summarize the results. I'll get to that soon, but first a brief diversion for more vocabulary.
We use the term cities and the function Cities
to denote a set of cities. But now I want to talk about multiple trials over a collection of sets of cities: a plural of a plural. English doesn't give us a good way to do that, so it would be nice to have a singular noun that is a synonym for "set of cities." We'll use the term "map" for this, and the function Maps
to create a collection of maps. Just like Cities
, the function Maps
will give the same result every time it is called with the same arguments.
def Maps(num_maps, num_cities):
"Return a list of maps, each consisting of the given number of cities."
return tuple(Cities(num_cities, seed=(m, num_cities))
for m in range(num_maps))
The term benchmarking means running a function on a standard collection of inputs, in order to compare its performance. We'll define a general-purpose function, benchmark
, which takes a function and a collection of inputs for that function, and runs the function on each of the inputs. It then returns two values: the average time taken per input, and the list of results of the function.
def benchmark(function, inputs):
"Run function on all the inputs; return pair of (average_time_taken, results)."
t0 = time.clock()
results = map(function, inputs)
t1 = time.clock()
average_time = (t1 - t0) / len(inputs)
return (average_time, results)
We can use benchmark
to see that the abolute value function takes less than a microsecond:
benchmark(abs, range(-10, 10))
And we can see that alltours_tsp
can handle 6-city maps in about a millisecond each:
benchmark(alltours_tsp, Maps(10, 6))
Here's an issue about benchmarking: each time we develop a new algorithm, we would like to compare its performance to some standard old algorithms. It seems a waste of computer time to re-run the old algorithms on a standard data set each time. And it seems like a waste of human time to scroll back up and check (e.g. "How did nn_tsp
do on Maps(30, 60)
? Where was that?"). It would be better if the benchmark
function could remember the (function, inputs) pairs that it has already run, and when asked for the same pair over again, just go look up the results rather than re-computing the results.
The process of taking a function (such as benchmark
) and making it remember its previously-computed results is called memoization. The idea is that we do:
benchmark = memoize(benchmark)
and now when we call benchmark(function, test_cases)
the first time it will be computed just like before, but when we make the same call a second time, the result will be instanly looked up rather than laboriously re-computed.
Here is one way to implement the function memoized
so that memoized(func)
creates a new function that acts just like func
except that it stores results in a table, called a cache:
def memoize(func):
"Return a version of func that remembers the output for each input."
cache = {}
def new_func(*args):
try:
# If we have already computed func(*args), just return it
return cache[args]
except TypeError:
# If args are not hashable, compute func(*args) and return it
return func(*args)
except KeyError:
# If args are hashable, compute func(*args) and store it
cache[args] = func(*args)
return cache[args]
new_func._cache = cache
new_func.__doc__ = func.__doc__
new_func.__name__ = func.__name__
return new_func
There are three paths through the function:
args
is already in the cache, then look it up and return it.TypeError
means that one of the args
is not hashable; in that case we just call func(*args)
and return it. We can't do caching in this case.KeyError
means that args
are hashable, but have not yet been seen. So compute func(*args)
, store the result in the cache, and return it.We also copy over the documentation and function name from the old func
to the new memoized function, and store the cache there as well, so that, if we want to to, we could do benchmark._cache.clear()
.
We could now do "benchmark = memoize(benchmark)
", or more Pythonically, use the decorator syntax:
@memoize
def benchmark(function, inputs):
"Run function on all the inputs; return pair of (average_time_taken, results)."
t0 = time.clock()
results = map(function, inputs)
t1 = time.clock()
average_time = (t1 - t0) / len(inputs)
return (average_time, results)
Here we see that the first call to benchmark
takes 5 seconds, while the second repeated call takes only microseconds but correctly delivers the same result:
%time benchmark(alltours_tsp, Maps(2, 10))
%time benchmark(alltours_tsp, Maps(2, 10))
Now let's add another function, benchmarks
, which builds on benchmark
in two ways:
benchmarks
.)TSP
algorithms, and rather than returning results, it prints summary statistics: the mean, standard deviation, min, and max of tour lengths, as well as the time taken and the number and size of the sets of cities.def benchmarks(tsp_algorithms, maps=Maps(30, 60)):
"Print benchmark statistics for each of the algorithms."
for tsp in tsp_algorithms:
time, results = benchmark(tsp, maps)
lengths = map(tour_length, results)
print("{:>25} |{:7.1f} Â±{:4.0f} ({:5.0f} to {:5.0f}) |{:7.3f} secs/map | {} â¨‰ {}-city maps"
.format(tsp.__name__, mean(lengths), stddev(lengths), min(lengths), max(lengths),
time, len(maps), len(maps[0])))
Here are two functions to do simple statistics:
def mean(numbers): return sum(numbers) / len(numbers)
def stddev(numbers):
"The standard deviation of a collection of numbers."
return (mean([x ** 2 for x in numbers]) - mean(numbers) ** 2) ** 0.5
nn_tsp
?¶Now we are in a position to gain some insight into how many repetitions, or starting cities, we need to get a good result from nn_tsp
.
def repeat_25_nn_tsp(cities): return repeated_nn_tsp(cities, 25)
def repeat_50_nn_tsp(cities): return repeated_nn_tsp(cities, 50)
algorithms = [nn_tsp, repeat_10_nn_tsp, repeat_25_nn_tsp, repeat_50_nn_tsp, repeat_100_nn_tsp]
benchmarks(algorithms)
We see that adding more starting cities results in shorter tours, but you start getting diminishing returns after 25 or 50 repetitions.
Let's try again with bigger maps:
benchmarks(algorithms, Maps(30, 120))
benchmarks(algorithms, Maps(30, 150))
The results are similar. So depending on what your priorities are (run time versus tour length), somewhere around 25 or 50 repetitions might be a good tradeoff.
Next let's try to analyze where nearest neighbors goes wrong, and see if we can do something about it.
Consider the 20-city map that we build below:
outliers_list = [City(2, 2), City(2, 3), City(2, 4), City(2, 5), City(2, 6),
City(3, 6), City(4, 6), City(5, 6), City(6, 6),
City(6, 5), City(6, 4), City(6, 3), City(6, 2),
City(5, 2), City(4, 2), City(3, 2),
City(1, 6.8), City(7.8, 6.4), City(7, 1.2), City(0.2, 2.8)]
outliers = set(outliers_list)
plot_lines(outliers, 'bo')
Let's see what a nearest neighbor search does on this map:
plot_tsp(nn_tsp, outliers)
The tour starts out going around the inner square. But then we are left with four long lines to pick up the outliers. Let's try to understand what went wrong. First we'll create a new tool to draw better diagrams:
def plot_labeled_lines(points, *args):
"""Plot individual points, labeled with an index number.
Then, args describe lines to draw between those points.
An arg can be a matplotlib style, like 'ro--', which sets the style until changed,
or it can be a list of indexes of points, like [0, 1, 2], saying what line to draw."""
# Draw points and label them with their index number
plot_lines(points, 'bo')
for (label, p) in enumerate(points):
plt.text(X(p), Y(p), ' '+str(label))
# Draw lines indicated by args
style = 'bo-'
for arg in args:
if isinstance(arg, str):
style = arg
else: # arg is a list of indexes into points, forming a line
Xs = [X(points[i]) for i in arg]
Ys = [Y(points[i]) for i in arg]
plt.plot(Xs, Ys, style)
plt.axis('scaled'); plt.axis('off'); plt.show()
In the diagram below, imagine we are running a nearest neighbor algorithm, and it has created a partial tour from city 0 to city 4. Now there is a choice. City 5 is the nearest neighbor. But if we don't take city 16 at this point, we will have to pay a higher price sometime later to pick up city 16.
plot_labeled_lines(outliers_list, 'bo-', [0, 1, 2, 3, 4], 'ro--', [4, 16], 'bo--', [4, 5])
It seems that picking up an outlier is sometimes a good idea, but sometimes going directly to the nearest neighbor is a better idea. So what can we do? It is difficult to make the choice between an outlier and a nearest neighbor while we are constructing a tour, because we don't have the context of the whole tour yet. So here's an alternative idea: don't try to make the right choice while constructing the tour; just go ahead and make any choice, then when the tour is complete, alter it to correct problems caused by outliers (or other causes).
We'll define a segment as a subsequence of a tour: a sequence of consecutive cities within a tour. A tour forms a loop, but a segment does not have a loop; it is open-ended on both ends. So, if [A, B, C, D]
is a 4-city tour, then segments include [A, B]
, [B, C, D]
, and many others. Note that the segment [A, B, C, D]
is different than the tour [A, B, C, D]
; the tour returns from D
to A
but the segment does not.
One way we could try to improve a tour is by reversing a segment. Consider this tour:
cross = [City(9, 3), City(3, 10), City(2, 16), City(3, 21), City(9, 28),
City(26, 3), City(32, 10), City(33, 16), City(32, 21), City(26, 28)]
plot_labeled_lines(cross, range(-1,10))
This is clearly not an optimal tour. We should "uncross" the lines, which can be achieved by reversing a segment. The tour as it stands is [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
. If we reverse the segment [5, 6, 7, 8, 9]
, we get the tour [0, 1, 2, 3, 4, 9, 8, 7, 6, 5]
, which is the optimal tour. In the diagram below, reversing [5, 6, 7, 8, 9]
is equivalent to deleting the red dashed lines and adding the green dotted lines. If the sum of the lengths of the green dotted lines is less than the sum of the lengths of the red dashed lines, then we know the reversal is an improvement.
plot_labeled_lines(cross, 'bo-', range(5), range(5, 10),
'g:', (4, 9), (0, 5),
'r--', (4, 5), (0, 9))
Here we see that reversing [5, 6, 7, 8, 9]
works:
tour = Tour(cross)
tour[5:10] = reversed(tour[5:10])
plot_tour(tour)
Here is how we can check if reversing a segment is an improvement, and if so to do it:
def reverse_segment_if_better(tour, i, j):
"If reversing tour[i:j] would make the tour shorter, then do it."
# Given tour [...A-B...C-D...], consider reversing B...C to get [...A-C...B-D...]
A, B, C, D = tour[i-1], tour[i], tour[j-1], tour[j % len(tour)]
# Are old edges (AB + CD) longer than new ones (AC + BD)? If so, reverse segment.
if distance(A, B) + distance(C, D) > distance(A, C) + distance(B, D):
tour[i:j] = reversed(tour[i:j])
Now let's write a function, alter_tour
, which finds segments to swap. What segments should we consider? I don't know how to be clever about the choice, but I do know how to be fairly thorough: try all segments of all lengths at all starting positions. I have an intuition that trying longer ones first is better (although I'm not sure).
I worry that even trying all segements won't be enough: after I reverse one segment, it might open up opportunities to reverse other segments. So, after trying all possible segments, I'll check the tour length. If it has been reduced, I'll go through the alter_tour
process again.
def alter_tour(tour):
"Try to alter tour for the better by reversing segments."
original_length = tour_length(tour)
for (start, end) in all_segments(len(tour)):
reverse_segment_if_better(tour, start, end)
# If we made an improvement, then try again; else stop and return tour.
if tour_length(tour) < original_length:
return alter_tour(tour)
return tour
def all_segments(N):
"Return (start, end) pairs of indexes that form segments of tour of length N."
return [(start, start + length)
for length in range(N, 2-1, -1)
for start in range(N - length + 1)]
Here is what the list of all segments look like, for N=4:
all_segments(4)
We can see that altering the cross tour does straighten it out:
plot_tour(alter_tour(Tour(cross)))
altered_nn_tsp
)¶Let's see what happens when we alter the output of nn_tsp
:
def altered_nn_tsp(cities):
"Run nearest neighbor TSP algorithm, and alter the results by reversing segments."
return alter_tour(nn_tsp(cities))
Let's try this new algorithm on some test cases:
plot_tsp(altered_nn_tsp, set(cross))
plot_tsp(altered_nn_tsp, Cities(9))
It gets the optimal results. Let's try benchmarking:
algorithms = [nn_tsp, repeat_50_nn_tsp, altered_nn_tsp]
benchmarks(algorithms)
This is quite encouraging; altered_nn_tsp
gives shorter tours and is three times faster than repeating nearest neighbors from 50 starting cities. Could we do better?
altered_repeated_nn_tsp
)¶We have seen that the nearest neighbor algorithm is improved by both the alteration and repetition strategies. So why not apply both strategies?
def repeated_altered_nn_tsp(cities, repetitions=20):
"Use alteration to improve each repetition of nearest neighbors."
return shortest_tour(alter_tour(nn_tsp(cities, start))
for start in sample(cities, repetitions))
def repeat_5_altered_nn_tsp(cities): return repeated_altered_nn_tsp(cities, 5)
Let's see it in action:
plot_tsp(repeated_altered_nn_tsp, Cities(100))
That looks like a good tour. Let's gather more data:
algorithms = [nn_tsp, repeat_50_nn_tsp, altered_nn_tsp, repeated_altered_nn_tsp]
benchmarks(algorithms)
print('-' * 100)
benchmarks(algorithms, Maps(30, 120))
So, repetition gives us a modest improvement in tour length over the altered_nn_tsp
, at the cost of 20 times more run time. It looks like the alteration strategy is more powerful than the repetition strategy in this case.
I thought it would be fun to work on some real maps, instead of random maps. First I found a page that lists geographical coordinates of US cities. Here is an excerpt from that page:
[TCL] 33.23 87.62 Tuscaloosa,AL [FLG] 35.13 111.67 Flagstaff,AZ [PHX] 33.43 112.02 Phoenix,AZ
I also found a blog post by Randal S. Olson who chose 50 landmarks across the states and found a tour based on actual road-travel distances, not straight-line distance. His data looks like this:
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD 43.879102 -103.459067 Toltec Mounds, Scott, AR 34.647037 -92.065143 Ashfall Fossil Bed, Royal, NE 42.425000 -98.158611You can't see, but fields are separated by tabs in this data.
Now we have a problem: we have two similar but different data formats, and we want to convert both of them to Maps
(sets of cities). Python provides a module, csv
(for "comma-separated values"), to parse data like this. The function csv.reader
takes an input that should be an iterable over lines of text, and optionally you can tell it what character to use as a delimiter (as well as several other options). For each line, it generates a
list of fields. For example, for the line "[TCL] 33.23 87.62 Tuscaloosa,AL"
it would generate the list ['[TCL]', '33.23', '87.62', 'Tuscaloosa,AL']
.
I define the function Coordinate_map
to take an iterable of lines (usually a file object or a list of strings), parse it with csv_reader
, pick out the latitude and longitude columns, and build a City
out of each one:
def lines(text): return text.strip().splitlines()
def Coordinate_map(lines, delimiter=' ', lat_col=1, long_col=2, lat_scale=69, long_scale=-48):
"""Make a set of Cities from an iterable of lines of text.
Specify the column delimiter, and the zero-based column number of lat and long.
Treat long/lat as a square x/y grid, scaled by long_scale and lat_scale.
Source can be a file object, or list of lines."""
return frozenset(City(long_scale * float(row[long_col]),
lat_scale * float(row[lat_col]))
for row in csv.reader(lines, delimiter=delimiter, skipinitialspace=True))
You might be wondering about the lat_scale=69, long_scale=-48
part. The issue is that we have latitude and longitude for cities, and we want to compute the distance between cities. To do that accurately requires complicated trigonometry. But we can get an approximation by assuming the earth is flat, and that latitude and longitude are on a rectangular grid. (This is a bad approximation if you're talking about distances of 10,000 miles, but close enough for 100 miles, as long as you're not too close to the poles.) I took the latitude of the center of the country (Wichita, KS: latitude 37.65) and plugged it into a Length Of A Degree Of Latitude
And Longitude Calculator to find that, in Wichita, one degree of latitude is 69 miles, and one degree of longitude is 48 miles. (It is -48 rather than +48 because the US is west of the prime meridian.)
Now let's create the map of USA cities, and find a tour for it:
USA_map = Coordinate_map(lines("""
[TCL] 33.23 87.62 Tuscaloosa,AL
[FLG] 35.13 111.67 Flagstaff,AZ
[PHX] 33.43 112.02 Phoenix,AZ
[PGA] 36.93 111.45 Page,AZ
[TUS] 32.12 110.93 Tucson,AZ
[LIT] 35.22 92.38 Little Rock,AR
[SFO] 37.62 122.38 San Francisco,CA
[LAX] 33.93 118.40 Los Angeles,CA
[SAC] 38.52 121.50 Sacramento,CA
[SAN] 32.73 117.17 San Diego,CA
[SBP] 35.23 120.65 San Luis Obi,CA
[EKA] 41.33 124.28 Eureka,CA
[DEN] 39.75 104.87 Denver,CO
[DCA] 38.85 77.04 Washington/Natl,DC
[MIA] 25.82 80.28 Miami Intl,FL
[TPA] 27.97 82.53 Tampa Intl,FL
[JAX] 30.50 81.70 Jacksonville,FL
[TLH] 30.38 84.37 Tallahassee,FL
[ATL] 33.65 84.42 Atlanta,GA
[BOI] 43.57 116.22 Boise,ID
[CHI] 41.90 87.65 Chicago,IL
[IND] 39.73 86.27 Indianapolis,IN
[DSM] 41.53 93.65 Des Moines,IA
[SUX] 42.40 96.38 Sioux City,IA
[ICT] 37.65 97.43 Wichita,KS
[LEX] 38.05 85.00 Lexington,KY
[NEW] 30.03 90.03 New Orleans,LA
[BOS] 42.37 71.03 Boston,MA
[PWM] 43.65 70.32 Portland,ME
[BGR] 44.80 68.82 Bangor,ME
[CAR] 46.87 68.02 Caribou Mun,ME
[DET] 42.42 83.02 Detroit,MI
[STC] 45.55 94.07 St Cloud,MN
[DLH] 46.83 92.18 Duluth,MN
[STL] 38.75 90.37 St Louis,MO
[JAN] 32.32 90.08 Jackson,MS
[BIL] 45.80 108.53 Billings,MT
[BTM] 45.95 112.50 Butte,MT
[RDU] 35.87 78.78 Raleigh-Durh,NC
[INT] 36.13 80.23 Winston-Salem,NC
[OMA] 41.30 95.90 Omaha/Eppley,NE
[LAS] 36.08 115.17 Las Vegas,NV
[RNO] 39.50 119.78 Reno,NV
[AWH] 41.33 116.25 Wildhorse,NV
[EWR] 40.70 74.17 Newark Intl,NJ
[SAF] 35.62 106.08 Santa Fe,NM
[NYC] 40.77 73.98 New York,NY
[BUF] 42.93 78.73 Buffalo,NY
[ALB] 42.75 73.80 Albany,NY
[FAR] 46.90 96.80 Fargo,ND
[BIS] 46.77 100.75 Bismarck,ND
[CVG] 39.05 84.67 Cincinnati,OH
[CLE] 41.42 81.87 Cleveland,OH
[OKC] 35.40 97.60 Oklahoma Cty,OK
[PDX] 45.60 122.60 Portland,OR
[MFR] 42.37 122.87 Medford,OR
[AGC] 40.35 79.93 Pittsburgh,PA
[PVD] 41.73 71.43 Providence,RI
[CHS] 32.90 80.03 Charleston,SC
[RAP] 44.05 103.07 Rapid City,SD
[FSD] 43.58 96.73 Sioux Falls,SD
[MEM] 35.05 90.00 Memphis Intl,TN
[TYS] 35.82 83.98 Knoxville,TN
[CRP] 27.77 97.50 Corpus Chrst,TX
[DRT] 29.37 100.92 Del Rio,TX
[IAH] 29.97 95.35 Houston,TX
[SAT] 29.53 98.47 San Antonio,TX
[LGU] 41.78 111.85 Logan,UT
[SLC] 40.78 111.97 Salt Lake Ct,UT
[SGU] 37.08 113.60 Saint George,UT
[CNY] 38.77 109.75 Moab,UT
[MPV] 44.20 72.57 Montpelier,VT
[RIC] 37.50 77.33 Richmond,VA
[BLI] 48.80 122.53 Bellingham,WA
[SEA] 47.45 122.30 Seattle,WA
[ALW] 46.10 118.28 Walla Walla,WA
[GRB] 44.48 88.13 Green Bay,WI
[MKE] 42.95 87.90 Milwaukee,WI
[CYS] 41.15 104.82 Cheyenne,WY
[SHR] 44.77 106.97 Sheridan,WY
"""))
plot_lines(USA_map, 'bo')
plot_tsp(repeated_altered_nn_tsp, USA_map)
Not bad! There are no obvious errors in the tour (although I'm not at all confident it is the optimal tour).
Now let's do the same for Randal Olson's landmarks. Note that the data is delimited by tabs, not spaces, and the longitude already has a minus sign, so we don't need another one in long_scale
.
USA_landmarks_map = Coordinate_map(lines("""
Mount Rushmore National Memorial, South Dakota 244, Keystone, SD 43.879102 -103.459067
Toltec Mounds, Scott, AR 34.647037 -92.065143
Ashfall Fossil Bed, Royal, NE 42.425000 -98.158611
Maryland State House, 100 State Cir, Annapolis, MD 21401 38.978828 -76.490974
The Mark Twain House & Museum, Farmington Avenue, Hartford, CT 41.766759 -72.701173
Columbia River Gorge National Scenic Area, Oregon 45.711564 -121.519633
Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY 37.186998 -86.100528
Bryce Canyon National Park, Hwy 63, Bryce, UT 37.593038 -112.187089
USS Alabama, Battleship Parkway, Mobile, AL 30.681803 -88.014426
Graceland, Elvis Presley Boulevard, Memphis, TN 35.047691 -90.026049
Wright Brothers National Memorial Visitor Center, Manteo, NC 35.908226 -75.675730
Vicksburg National Military Park, Clay Street, Vicksburg, MS 32.346550 -90.849850
Statue of Liberty, Liberty Island, NYC, NY 40.689249 -74.044500
Mount Vernon, Fairfax County, Virginia 38.729314 -77.107386
Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND 48.000160 -104.041483
San Andreas Fault, San Benito County, CA 36.576088 -120.987632
Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086 34.457043 -97.012213
Hanford Site, Benton County, WA 46.550684 -119.488974
Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH 39.174331 -84.524997
Craters of the Moon National Monument & Preserve, Arco, ID 43.416650 -113.516650
The Alamo, Alamo Plaza, San Antonio, TX 29.425967 -98.486142
New Castle Historic District, Delaware 38.910832 -75.527670
Gateway Arch, Washington Avenue, St Louis, MO 38.624647 -90.184992
West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN 38.566697 -86.617524
Carlsbad Caverns National Park, Carlsbad, NM 32.123169 -104.587450
Pikes Peak, Colorado 38.840871 -105.042260
Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA 31.056794 -82.272327
Cape Canaveral, FL 28.388333 -80.603611
Glacier National Park, West Glacier, MT 48.759613 -113.787023
Congress Hall, Congress Place, Cape May, NJ 08204 38.931843 -74.924184
Olympia Entertainment, Woodward Avenue, Detroit, MI 42.387579 -83.084943
Fort Snelling, Tower Avenue, Saint Paul, MN 44.892850 -93.180627
Hoover Dam, Boulder City, CO 36.012638 -114.742225
White House, Pennsylvania Avenue Northwest, Washington, DC 38.897676 -77.036530
USS Constitution, Boston, MA 42.372470 -71.056575
Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH 44.258120 -71.441189
Grand Canyon National Park, Arizona 36.106965 -112.112997
The Breakers, Ochre Point Avenue, Newport, RI 41.469858 -71.298265
Fort Sumter National Monument, Sullivan's Island, SC 32.752348 -79.874692
Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108 37.794781 -122.411715
Yellowstone National Park, WY 82190 44.462085 -110.642441
French Quarter, New Orleans, LA 29.958443 -90.064411
C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS 39.317245 -94.909536
Shelburne Farms, Harbor Road, Shelburne, VT 44.408948 -73.247227
Taliesin, County Road C, Spring Green, Wisconsin 43.141031 -90.070467
Acadia National Park, Maine 44.338556 -68.273335
Liberty Bell, 6th Street, Philadelphia, PA 39.949610 -75.150282
Terrace Hill, Grand Avenue, Des Moines, IA 41.583218 -93.648542
Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL 39.797501 -89.646211
Lost World Caverns, Lewisburg, WV 37.801788 -80.445630
"""), delimiter='\t', long_scale=48)
plot_lines(USA_landmarks_map, 'bo')
plot_tsp(repeated_altered_nn_tsp, USA_landmarks_map)
We can compare that to the tour that Randal Olson computed as the shortest based on road distances:
The two tours are similar but not the same. I think the difference is that roads through the rockies and along the coast of the Carolinas tend to be very windy, so Randal's tour avoids them, whereas my straight-line tour does not. William Cook provides an analysis, and a tour that is shorter than either Randal's or mine.
Now let's go back to the original web page to get a bigger map with over 1000 cities. I note that the page has some lines that I don't want, so I will filter out lines that are not in the continental US (that is, cities in Alaska or Hawaii), as well as header lines that do not start with '['
.
url = 'http://www.realestate3d.com/gps/latlong.htm'
def continental_USA(line):
"Does line denote a city in the continental United States?"
return line.startswith('[') and ',AK' not in line and ',HI' not in line
USA_big_map = Coordinate_map(filter(continental_USA, urllib.urlopen(url)))
plot_lines(USA_big_map, 'bo')
Let's get a baseline tour with nn_tsp
:
plot_tsp(nn_tsp, USA_big_map)
Now try to improve on that with repeat_100_nn_tsp
and with repeat_5_altered_nn_tsp
(which will take a while with over 1000 cities):
plot_tsp(repeat_100_nn_tsp, USA_big_map)
plot_tsp(repeat_5_altered_nn_tsp, USA_big_map)
Again we see that we do better by spending our run time budget on alteration rather than on repetition. This time we saved over 8,000 miles of travel in half a minute!
greedy_tsp
)¶At the start of the Approximate Algorithms section, we mentioned two ideas:
It is time to develop the greedy algorithm, so-called because at every step it greedily adds to the tour the edge that is shortest (even if that is not best in terms of long-range planning). The nearest neighbor algorithm always extended the tour by adding on to the end. The greedy algorithm is different in that it doesn't have a notion of end of the tour; instead it keeps a set of partial segments. Here's a brief statement of the algorithm:
Greedy Algorithm: Maintain a set of segments; intially each city defines its own 1-city segment. Find the shortest possible edge that connects two endpoints of two different segments, and join those segments with that edge. Repeat until we form a segment that tours all the cities.
On each step of the algorithm, we want to "find the shortest possible edge that connects two endpoints." That seems like an expensive operation to do on each step. So we will add in some data structures to enable us to speed up the computation. Here's a more detailed sketch of the algorithm:
(A, B)
then it does not contain (B, A)
, and it never contains (A, A)
.{A: [A, B, C, D], D: [A, B, C, D]}
. Initially, each city is the endpoint of its own 1-city-long segment, but as we join segments together, some cities are no longer endpoints and are removed from the dict.(A, B)
such that both A
and B
are endpoints of different segments, then join the two segments together. Maintain the endpoints dict to reflect this new segment. Stop when you create
a segment that contains all the cities.Let's consider an example: assume we have seven cities, labeled A through G. Suppose CG happens to be the shortest edge. We would add the edge to the partial tour, by joining the segment that contains C with the segment that contains G. In this case, the joining is easy, because each segment is one city long; we join them to form a segment two cities long. We then look at the next shortest edge and continue the process, joining segments as we go, as shown in the table below. Some edges cannot be used. For example, FD cannot be used, because by the time it becomes the shortest edge, D is already in the interior of a segment. Next, AE cannot be used, even though both A and E are endpoints, because it would make a loop out of ACGDE. Finally, note that sometimes we may have to reverse a segment. For example, EF can merge AGCDE and BF, but first we have to reverse BF to FB.
Shortest Edge | Usage of edge | Resulting Segments |
---|---|---|
— | — | A; B; C; D; E; F; G |
CG | Join C to G | A; B; CG; D; E; F |
DE | Join D to E | A; B; CG; DE; F |
AC | Join A to CG | B; ACG; DE; F |
GD | Join ACG to D | B; ACGDE; F |
FD | Discard | B; ACGDE; F |
AE | Discard | B; ACGDE; F |
BF | Join B to F | BF; ACGDE |
CF | Discard | BF; ACGDE |
EF | Join ACGDE to FB | ACGDEFB |
Here is the code:
def greedy_tsp(cities):
"""Go through edges, shortest first. Use edge to join segments if possible."""
edges = shortest_edges_first(cities) # A list of (A, B) pairs
endpoints = {c: [c] for c in cities} # A dict of {endpoint: segment}
for (A, B) in edges:
if A in endpoints and B in endpoints and endpoints[A] != endpoints[B]:
new_segment = join_endpoints(endpoints, A, B)
if len(new_segment) == len(cities):
return new_segment
# TO DO: functions: shortest_edges_first, join_endpoints
Note: The endpoints
dict is serving two purposes. First, the keys of the dict are all the cities that are endpoints of some segments,
making it possible to ask "A in endpoints
" to see if city A
is an endpoint. Second, the values of the dict are all the segments, making it possible to ask "endpoints[A] != endpoints[B]
" to make sure that the two cities endpoints of different segments, not of the same segment.
The shortest_edges_first
function is easy: generate all (A, B)
pairs of cities, and sort by the distance between the cities. (Note: I use the conditional if id(A) < id(B)
so that I won't have both (A, B)
and (B, A)
in my list of edges, and I won't ever have (A, A)
.)
def shortest_edges_first(cities):
"Return all edges between distinct cities, sorted shortest first."
edges = [(A, B) for A in cities for B in cities
if id(A) < id(B)]
return sorted(edges, key=lambda edge: distance(*edge))
For the join_endpoints
function, I first make sure that A is the last element of one segment and B is the first element of the other, by reversing segments if necessary. Then I add the B segment on to the end of the A segment. Finally, I update the endpoints
dict. This is a bit tricky! My first thought was that A and B are no longer endpoints, because they have been joined together in the interior of the segment. However, that isn't always true. If A was the endpoint of a 1-city segment, then when you join it to B, A is still an endpoint. I could have had complicated logic to handle the case when A, B, or both, or neither were 1-city segments, but I decided on a different tactic: first unconditionally delete A and B from the endpoints dict, no matter what. Then add the two endpoints of the new segment (which is Asegment
) to the endpoints dict. One or both of them might be A or B, but we don't need conditional logic to check.
def join_endpoints(endpoints, A, B):
"Join B's segment onto the end of A's and return the segment. Maintain endpoints dict."
Asegment, Bsegment = endpoints[A], endpoints[B]
if Asegment[-1] is not A: Asegment.reverse()
if Bsegment[0] is not B: Bsegment.reverse()
Asegment.extend(Bsegment)
del endpoints[A], endpoints[B]
endpoints[Asegment[0]] = endpoints[Asegment[-1]] = Asegment
return Asegment
Let's try out the greedy_tsp
algorithm on the two USA maps:
plot_tsp(greedy_tsp, USA_map)
plot_tsp(greedy_tsp, USA_big_map)
The greedy algorithm is worse than nearest neighbors on the small map, but better on the big one. Let's see if the alteration strategy can help:
def altered_greedy_tsp(cities):
"Run greedy TSP algorithm, and alter the results by reversing segments."
return alter_tour(greedy_tsp(cities))
plot_tsp(altered_greedy_tsp, USA_map)
plot_tsp(altered_greedy_tsp, USA_big_map)