blog/algorithms/11-paths/2024-01-01-bf-to-astar/01-bf.md

---
id: bf
slug: /paths/bf-to-astar/bf
title: BF
description: |
  Solving the shortest path problem with a naïve approach that turns into
  something.
tags:
  - cpp
  - brute force
  - bellman ford
  - dynamic programming
last_update:
  date: 2024-01-01
---

## Basic idea

We will _ease in_ with our own algorithm to find the shortest path. We will
start by thinking about the ways we can achieve that. If we didn't have the `*`
cells, we could've easily run a BFS[^1] and be done with it. Maybe it is a good
place to start, or isn't, there is only one way to find out though.

_How does the BFS work?_ We know the vertex where we start and we know the
vertex we want to find the shortest path to. Given this knowledge we
incrementally visit all of our neighbours and we do that over and over until the
destination is found[^2]. Could we leverage this somehow?

## Naïve approach

Well, we could probably start with all vertices being _unreachable_ (having the
highest possible price) and try to improve what we've gotten so far until there
are no improvements. That sounds fine, we shall implement this. Since we are
going on repeat, we will name this function `bf()` as in _brute-force_, cause it
is trying to find it the hard way:

```cpp
const static std::vector<vertex_t> DIRECTIONS =
    std::vector{std::make_pair(0, 1), std::make_pair(0, -1),
                std::make_pair(1, 0), std::make_pair(-1, 0)};

auto bf(const graph& g, const vertex_t& source, const vertex_t& destination)
    -> int {
  // ‹source› must be within the bounds
  assert(g.has(source));

  // ‹destination› must be within the bounds
  assert(g.has(destination));

  // we need to initialize the distances
  std::vector<std::vector<int>> distances(
      g.height(), std::vector(g.width(), graph::unreachable()));

  // ‹source› destination denotes the beginning where the cost is 0
  auto [sx, sy] = source;
  distances[sy][sx] = 0;

  // now we need to improve the paths as long as possible
  bool improvement_found;
  do {
    // reset the flag at the beginning
    improvement_found = false;

    // go through all of the vertices
    for (int y = 0; y < g.height(); ++y) {
      for (int x = 0; x < g.width(); ++x) {
        // skip the cells we cannot reach
        if (distances[y][x] == graph::unreachable()) {
          continue;
        }

        // go through the neighbours
        auto u = std::make_pair(x, y);
        for (const auto& [dx, dy] : DIRECTIONS) {
          auto v = std::make_pair(x + dx, y + dy);
          auto cost = g.cost(u, v);

          // if we can move to the cell and it's better, relax¹ it
          if (cost != graph::unreachable() &&
              distances[y][x] + cost < distances[y + dy][x + dx]) {
            distances[y + dy][x + dx] = distances[y][x] + cost;
            improvement_found = true;
          }
        }
      }
    }
  } while (improvement_found);

  return distances[destination.second][destination.first];
}
```

:::info Relaxation

I have made a brief mention of the relaxation in the comment in the code. You've
been probably taught that **relaxation of an edge** means that you found
a better solution to the problem.

In general it is an approximation technique that _reduces_ the problem of
finding the path `u → x1 → … → xn → v` to subproblems
`u → x1, x1 → x2, …, xn → v` such that the sum of the costs of each step is
**minimal**.

:::

### Correctness

_Is our solution correct?_ It appears to be correct… We have rather complicated
map and our algorithm has finished in an instant with the following output:

```
Normal cost: 1
Vortex cost: 5
Graph:
#############
#..#..*.*.**#
##***.....**#
#..########.#
#...###...#.#
#..#...##.#.#
#..#.*.#..#.#
#D...#....#.#
########*.*.#
#S..........#
#############
Cost: 22
```

If you have a better look at the map, you will realize that the cost `22` is the
one path skipping the `*` cells, since they cost more than going around.

We can play around a bit with it. The `*` cells can even be vortices that pull
you in with a negative price and let you _propel_ yourself out :wink: Let's
change their cost to `-1` then and see what's the fastest path to our goal.

```
Normal cost: 1
Vortex cost: -1
Graph:
#############
#..#..*.*.**#
##***.....**#
#..########.#
#...###...#.#
#..#...##.#.#
#..#.*.#..#.#
#D...#....#.#
########*.*.#
#S..........#
#############
```

And we're somehow stuck… The issue comes from the fact that _spinning around_ in
the vortices allows us to lower the cost infinitely. That's why after each
iteration there is still a possibility to lower the cost, hence the algorithm
doesn't finish. _What can we do about this?_

:::tip

This algorithm is correct as long as there are no negative loops, i.e. ways how
to lower the cost infinitely. Therefore we can also just lay a precondition that
requires no negative loops to be present.

:::

### Fixing the infinite loop

Our issue lies in the fact that we can endlessly lower the cost. Such thing must
surely happen in some kind of a loop. We could probably track the relaxations
and once we spot repeating patterns, we know we can safely terminate with _some_
results at least.

This approach will not even work on our 2D map, let alone any graph. Problem is
that the _negative loops_ lower the cost in **each** iteration and that results
in lowering of the costs to the cells that are reachable from the said loops.
That's why this problem is relatively hard to tackle, it's not that easy to spot
the repeating patterns algorithmically.

On the other hand, we can approach this from the different perspective. Let's
assume the worst-case scenario (generalized for any graph):

> Let $K_n$ be complete graph. Let $P$ be the shortest path from $v_1$ to $v_n$
> such that $P$ has $n - 1$ edges, i.e. the shortest path between the two chosen
> vertices visits all vertices (not necessarily in order) and has the lowest
> cost.
>
> In such scenario assume the worst-case ordering of the relaxations (only one
> _helpful_ relaxation per iteration). In this case, in each iteration we find
> the next edge on our path $P$ as the last. This means that we need
> $\vert V \vert - 1$ iterations to find the shortest path $P$.
>
> Because we have laid $P$ as the shortest path from $v_1$ to $v_n$ and it
> visits all vertices, its prefixes are the shortest paths from $v_1$ to any
> other vertex in our graph.
>
> Therefore, we can safely assume that any relaxation after $\vert V \vert - 1$
> iterations, is the effect of a negative loop in the graph.

_How can we leverage this?_ We will go through the edges only as many times as
cells we have. Let's adjust the code to fix the looping:

```cpp
auto bf_finite(const graph& g, const vertex_t& source,
               const vertex_t& destination) -> int {
  // ‹source› must be within the bounds
  assert(g.has(source));

  // ‹destination› must be within the bounds
  assert(g.has(destination));

  // we need to initialize the distances
  std::vector<std::vector<int>> distances(
      g.height(), std::vector(g.width(), graph::unreachable()));

  // ‹source› destination denotes the beginning where the cost is 0
  auto [sx, sy] = source;
  distances[sy][sx] = 0;

  // now we only iterate as many times as cells that we have
  for (int i = g.height() * g.width(); i > 0; --i) {
    // go through all of the vertices
    for (int y = 0; y < g.height(); ++y) {
      for (int x = 0; x < g.width(); ++x) {
        // skip the cells we cannot reach
        if (distances[y][x] == graph::unreachable()) {
          continue;
        }

        // go through the neighbours
        auto u = std::make_pair(x, y);
        for (const auto& [dx, dy] : DIRECTIONS) {
          auto v = std::make_pair(x + dx, y + dy);
          auto cost = g.cost(u, v);

          // if we can move to the cell and it's better, relax¹ it
          if (cost != graph::unreachable() &&
              distances[y][x] + cost < distances[y + dy][x + dx]) {
            distances[y + dy][x + dx] = distances[y][x] + cost;
          }
        }
      }
    }
  }

  return distances[destination.second][destination.first];
}
```

And we get the following result:

```
Normal cost: 1
Vortex cost: -1
Graph:
#############
#..#..*.*.**#
##***.....**#
#..########.#
#...###...#.#
#..#...##.#.#
#..#.*.#..#.#
#D...#....#.#
########*.*.#
#S..........#
#############
Cost: -236
```

The negative cost means that there is a way to _propel_ ourselves via some
vortices. Let's adjust the cost of _vortices_ back to the original `5` and check
whether our modified algorithm works as it did before. And it surely does yield
the `22` as before.

:::tip Refactoring

You can definitely notice some _deep nesting_ in our code, to counter this
phenomenon I will convert the looping over `x` and `y` to one variable that can
be decomposed to `x` and `y`. It is a very common practice when working with 2D
arrays/lists to represent them as 1D. In our case:

```
i : 0 → width * height - 1
x = i % width
y = i / width
```

:::

## Bellman-Ford

If you have ever attended any Algorithms course that had path-finding in its
syllabus, you probably feel like you've seen the algorithm above before[^3]… And
yes, the first algorithm I have proposed is a very dumb version of the
_Bellman-Ford_ algorithm, it's dumb, because it loops :wink: After our “looping”
prevention we got to the point that is almost the _Bellman-Ford_ with the one
exception that it doesn't report whether there are any negative cycles, it just
ends.

Let's have a look at a proper implementation of the Bellman-Ford algorithm:

```cpp
auto bellman_ford(const graph& g, const vertex_t& source)
    -> std::vector<std::vector<int>> {
  // ‹source› must be within the bounds
  assert(g.has(source));

  // we need to initialize the distances
  std::vector<std::vector<int>> distances(
      g.height(), std::vector(g.width(), graph::unreachable()));

  // ‹source› destination denotes the beginning where the cost is 0
  auto [sx, sy] = source;
  distances[sy][sx] = 0;

  // now we only iterate as many times as cells that we have
  for (int i = g.height() * g.width(); i > 0; --i) {
    // go through all of the vertices
    for (int v = g.height() * g.width() - 1; v >= 0; --v) {
      int y = v / g.width();
      int x = v % g.width();

      // skip the cells we cannot reach
      if (distances[y][x] == graph::unreachable()) {
        continue;
      }

      // go through the neighbours
      auto u = std::make_pair(x, y);
      for (const auto& [dx, dy] : DIRECTIONS) {
        auto v = std::make_pair(x + dx, y + dy);
        auto cost = g.cost(u, v);

        // if we can move to the cell and it's better, relax¹ it
        if (cost != graph::unreachable() &&
            distances[y][x] + cost < distances[y + dy][x + dx]) {
          distances[y + dy][x + dx] = distances[y][x] + cost;
        }
      }
    }
  }

  // now we check for the negative loops
  bool relaxed = false;
  for (int v = g.height() * g.width() - 1; !relaxed && v >= 0; --v) {
    int y = v / g.width();
    int x = v % g.width();

    // skip the cells we cannot reach
    if (distances[y][x] == graph::unreachable()) {
      continue;
    }

    // go through the neighbours
    auto u = std::make_pair(x, y);
    for (const auto& [dx, dy] : DIRECTIONS) {
      auto v = std::make_pair(x + dx, y + dy);
      auto cost = g.cost(u, v);

      // if we can move to the cell and it's better, relax¹ it
      if (cost != graph::unreachable() &&
          distances[y][x] + cost < distances[y + dy][x + dx]) {
        relaxed = true;
        std::cerr << "Found a negative loop\n";
        break;
      }
    }
  }

  return distances;
}
```

And if we run it with our negative cost of entering vortices:

```
[Bellman-Ford] Found a negative loop
[Bellman-Ford] Cost: -240
```

### On the Bellman-Ford

You might be surprised that we have managed to iterate from a brute-force method
that mindlessly tries to find a better path until there are no better paths left
all the way to the Bellman-Ford algorithm.

I always say that Bellman-Ford is a _smart_ brute-force. BF is also an algorithm
that leverages _dynamic programming_. You might wonder how can it utilize DP if
it is “technically” a brute-force technique. Table with the shortest distances
is the thing that makes it DP.

> I might not know the shortest path yet, but I do remember all of other paths,
> and I can improve them, if possible.

That's where the beauty of both _dynamic programming_ and _relaxing_ gets merged
together and does its magic.

Proof of the correctness of the BF is done via induction to the number of
iterations. I would suggest to try to prove the correctness yourself and
possibly look it up, if necessary.

Also the correctness of the BF relies on the conclusion we've made when fixing
the infinite-loop on our naïve BF solution.

## Time complexity

Let's have a short look at the time complexities of the presented algorithms:

1. naïve approach: given that there are no negative loops, we are bound by the
   worst-case ordering of the relaxations which results in

   $$
   \mathcal{O}(\vert V \vert \cdot \vert E \vert)
   $$

2. our naïve approach with the fixed count of iterations instead of the
   `do-while` loop results in the same worst-case time complexity:

   $$
   \Theta(\vert V \vert \cdot \vert E \vert)
   $$

3. and finally the well-known Bellman-Ford's algorithm time complexity:
   $$
   \Theta(\vert V \vert \cdot \vert E \vert)
   $$

## Small refactor

Since we are literally copy-pasting the body of the loops just for the sake of
relaxing, we can factor that part out into a separate function:

```cpp
static auto _check_vertex(const graph& g,
                          std::vector<std::vector<int>>& distances, int v,
                          bool check_only = false) -> bool {
  bool improvement_found = false;

  // unpack the vertex coordinates
  int y = v / g.width();
  int x = v % g.width();

  // skip the cells we cannot reach
  if (distances[y][x] == graph::unreachable()) {
    return false;
  }

  // go through the neighbours
  auto u = std::make_pair(x, y);
  for (const auto& [dx, dy] : DIRECTIONS) {
    auto v = std::make_pair(x + dx, y + dy);
    auto cost = g.cost(u, v);

    // if we can move to the cell and it's better, relax¹ it
    if (cost != graph::unreachable() &&
        distances[y][x] + cost < distances[y + dy][x + dx]) {
      if (check_only) {
        return true;
      }

      distances[y + dy][x + dx] = distances[y][x] + cost;
      improvement_found = true;
    }
  }

  return improvement_found;
}
```

This function can be also used for checking the negative loops at the end of the
BF by using the `check_only` parameter to signal that we just want to know if
there would be any edge relaxed instead of performing the relaxation itself.

Then we can also see the differences between the specific versions of our
path-finding algorithms in a clear way:

```cpp
auto bf(const graph& g, const vertex_t& source, const vertex_t& destination)
    -> int {
  // ‹source› must be within the bounds
  assert(g.has(source));

  // ‹destination› must be within the bounds
  assert(g.has(destination));

  // we need to initialize the distances
  std::vector<std::vector<int>> distances(
      g.height(), std::vector(g.width(), graph::unreachable()));

  // ‹source› destination denotes the beginning where the cost is 0
  auto [sx, sy] = source;
  distances[sy][sx] = 0;

  // now we need to improve the paths as long as possible
  bool improvement_found;
  do {
    // reset the flag at the beginning
    improvement_found = false;

    // go through all of the vertices
    for (int v = g.height() * g.width() - 1; v >= 0; --v) {
      improvement_found = _check_vertex(g, distances, v) || improvement_found;
    }
  } while (improvement_found);

  return distances[destination.second][destination.first];
}

auto bf_finite(const graph& g, const vertex_t& source,
               const vertex_t& destination) -> int {
  // ‹source› must be within the bounds
  assert(g.has(source));

  // ‹destination› must be within the bounds
  assert(g.has(destination));

  // we need to initialize the distances
  std::vector<std::vector<int>> distances(
      g.height(), std::vector(g.width(), graph::unreachable()));

  // ‹source› destination denotes the beginning where the cost is 0
  auto [sx, sy] = source;
  distances[sy][sx] = 0;

  // now we only iterate as many times as cells that we have
  for (int i = g.height() * g.width(); i > 0; --i) {
    // go through all of the vertices
    for (int v = g.height() * g.width() - 1; v >= 0; --v) {
      _check_vertex(g, distances, v);
    }
  }

  return distances[destination.second][destination.first];
}

auto bellman_ford(const graph& g, const vertex_t& source)
    -> std::vector<std::vector<int>> {
  // ‹source› must be within the bounds
  assert(g.has(source));

  // we need to initialize the distances
  std::vector<std::vector<int>> distances(
      g.height(), std::vector(g.width(), graph::unreachable()));

  // ‹source› destination denotes the beginning where the cost is 0
  auto [sx, sy] = source;
  distances[sy][sx] = 0;

  // now we only iterate as many times as cells that we have
  for (int i = g.height() * g.width(); i > 0; --i) {
    // go through all of the vertices
    for (int v = g.height() * g.width() - 1; v >= 0; --v) {
      _check_vertex(g, distances, v);
    }
  }

  // now we check for the negative loops
  for (int v = g.height() * g.width() - 1; v >= 0; --v) {
    if (_check_vertex(g, distances, v, true)) {
      std::cerr << "[Bellman-Ford] Found a negative loop\n";
      break;
    }
  }

  return distances;
}

```

---

:::tip

You might've noticed that I've been using abbreviation _BF_ interchangeably for
both _Bellman-Ford_ and _brute-force_. If you think about the way Bellman-Ford
algorithm works, you should realize that in the worst case it's updating the
shortest path till there no shorter path exists, so in a sense, you could really
consider it a brute-force algorithm.

:::

[^1]: [Breadth-first search](https://en.wikipedia.org/wiki/Breadth-first_search)
[^2]:
    Of course, there are some technicalities like keeping track of the visited
    vertices to not taint the shortest path by already visited vertices.

[^3]: or at least you should, LOL