Backtracking algorithms¶
Backtracking algorithm is a method to solve problems by exhaustive search. Its core concept is to start from an initial state and brutally search for all possible solutions. The algorithm records the correct ones until a solution is found or all possible solutions have been tried but no solution can be found.
Backtracking typically employs "depth-first search" to traverse the solution space. In the "Binary tree" chapter, we mentioned that pre-order, in-order, and post-order traversals are all depth-first searches. Next, we are going to use pre-order traversal to solve a backtracking problem. This helps us to understand how the algorithm works gradually.
Example One
Given a binary tree, search and record all nodes with a value of \(7\) and return them in a list.
To solve this problem, we traverse this tree in pre-order and check if the current node's value is \(7\). If it is, we add the node's value to the result list res. The process is shown in the figure below:
Trial and retreat¶
It is called a backtracking algorithm because it uses a "trial" and "retreat" strategy when searching the solution space. During the search, whenever it encounters a state where it can no longer proceed to obtain a satisfying solution, it undoes the previous choice and reverts to the previous state so that other possible choices can be chosen for the next attempt.
In Example One, visiting each node starts a "trial". And passing a leaf node or the return statement to going back to the parent node suggests "retreat".
It's worth noting that retreat is not merely about function returns. We'll expand slightly on Example One question to explain what it means.
Example Two
In a binary tree, search for all nodes with a value of \(7\) and for all matching nodes, please return the paths from the root node to that node.
Based on the code from Example One, we need to use a list called path to record the visited node paths. When a node with a value of \(7\) is reached, we copy path and add it to the result list res. After the traversal, res holds all the solutions. The code is as shown:
In each "trial", we record the path by adding the current node to path. Whenever we need to "retreat", we pop the node from path to restore the state prior to this failed attempt.
By observing the process shown in the figure below, the trial is like "advancing", and retreat is like "undoing". The later pairs can be seen as a reverse operation to their counterpart.
Prune¶
Complex backtracking problems usually involve one or more constraints, which are often used for "pruning".
Example Three
In a binary tree, search for all nodes with a value of \(7\) and return the paths from the root to these nodes, with restriction that the paths do not contain nodes with a value of \(3\).
To meet the above constraints, we need to add a pruning operation: during the search process, if a node with a value of \(3\) is encountered, it aborts further searching down through the path immediately. The code is as shown:
"Pruning" is a very vivid noun. As shown in the figure below, in the search process, we "cut off" the search branches that do not meet the constraints. It avoids further unnecessary attempts, thus enhances the search efficiency.
Framework code¶
Now, let's try to distill the main framework of "trial, retreat, and prune" from backtracking to enhance the code's universality.
In the following framework code, state represents the current state of the problem, choices represents the choices available under the current state:
def backtrack(state: State, choices: list[choice], res: list[state]):
"""Backtracking algorithm framework"""
# Check if it's a solution
if is_solution(state):
# Record the solution
record_solution(state, res)
# Stop searching
return
# Iterate through all choices
for choice in choices:
# Prune: check if the choice is valid
if is_valid(state, choice):
# Trial: make a choice, update the state
make_choice(state, choice)
backtrack(state, choices, res)
# Retreat: undo the choice, revert to the previous state
undo_choice(state, choice)
/* Backtracking algorithm framework */
void backtrack(State *state, vector<Choice *> &choices, vector<State *> &res) {
// Check if it's a solution
if (isSolution(state)) {
// Record the solution
recordSolution(state, res);
// Stop searching
return;
}
// Iterate through all choices
for (Choice choice : choices) {
// Prune: check if the choice is valid
if (isValid(state, choice)) {
// Trial: make a choice, update the state
makeChoice(state, choice);
backtrack(state, choices, res);
// Retreat: undo the choice, revert to the previous state
undoChoice(state, choice);
}
}
}
/* Backtracking algorithm framework */
void backtrack(State state, List<Choice> choices, List<State> res) {
// Check if it's a solution
if (isSolution(state)) {
// Record the solution
recordSolution(state, res);
// Stop searching
return;
}
// Iterate through all choices
for (Choice choice : choices) {
// Prune: check if the choice is valid
if (isValid(state, choice)) {
// Trial: make a choice, update the state
makeChoice(state, choice);
backtrack(state, choices, res);
// Retreat: undo the choice, revert to the previous state
undoChoice(state, choice);
}
}
}
/* Backtracking algorithm framework */
void Backtrack(State state, List<Choice> choices, List<State> res) {
// Check if it's a solution
if (IsSolution(state)) {
// Record the solution
RecordSolution(state, res);
// Stop searching
return;
}
// Iterate through all choices
foreach (Choice choice in choices) {
// Prune: check if the choice is valid
if (IsValid(state, choice)) {
// Trial: make a choice, update the state
MakeChoice(state, choice);
Backtrack(state, choices, res);
// Retreat: undo the choice, revert to the previous state
UndoChoice(state, choice);
}
}
}
/* Backtracking algorithm framework */
func backtrack(state *State, choices []Choice, res *[]State) {
// Check if it's a solution
if isSolution(state) {
// Record the solution
recordSolution(state, res)
// Stop searching
return
}
// Iterate through all choices
for _, choice := range choices {
// Prune: check if the choice is valid
if isValid(state, choice) {
// Trial: make a choice, update the state
makeChoice(state, choice)
backtrack(state, choices, res)
// Retreat: undo the choice, revert to the previous state
undoChoice(state, choice)
}
}
}
/* Backtracking algorithm framework */
func backtrack(state: inout State, choices: [Choice], res: inout [State]) {
// Check if it's a solution
if isSolution(state: state) {
// Record the solution
recordSolution(state: state, res: &res)
// Stop searching
return
}
// Iterate through all choices
for choice in choices {
// Prune: check if the choice is valid
if isValid(state: state, choice: choice) {
// Trial: make a choice, update the state
makeChoice(state: &state, choice: choice)
backtrack(state: &state, choices: choices, res: &res)
// Retreat: undo the choice, revert to the previous state
undoChoice(state: &state, choice: choice)
}
}
}
/* Backtracking algorithm framework */
function backtrack(state, choices, res) {
// Check if it's a solution
if (isSolution(state)) {
// Record the solution
recordSolution(state, res);
// Stop searching
return;
}
// Iterate through all choices
for (let choice of choices) {
// Prune: check if the choice is valid
if (isValid(state, choice)) {
// Trial: make a choice, update the state
makeChoice(state, choice);
backtrack(state, choices, res);
// Retreat: undo the choice, revert to the previous state
undoChoice(state, choice);
}
}
}
/* Backtracking algorithm framework */
function backtrack(state: State, choices: Choice[], res: State[]): void {
// Check if it's a solution
if (isSolution(state)) {
// Record the solution
recordSolution(state, res);
// Stop searching
return;
}
// Iterate through all choices
for (let choice of choices) {
// Prune: check if the choice is valid
if (isValid(state, choice)) {
// Trial: make a choice, update the state
makeChoice(state, choice);
backtrack(state, choices, res);
// Retreat: undo the choice, revert to the previous state
undoChoice(state, choice);
}
}
}
/* Backtracking algorithm framework */
void backtrack(State state, List<Choice>, List<State> res) {
// Check if it's a solution
if (isSolution(state)) {
// Record the solution
recordSolution(state, res);
// Stop searching
return;
}
// Iterate through all choices
for (Choice choice in choices) {
// Prune: check if the choice is valid
if (isValid(state, choice)) {
// Trial: make a choice, update the state
makeChoice(state, choice);
backtrack(state, choices, res);
// Retreat: undo the choice, revert to the previous state
undoChoice(state, choice);
}
}
}
/* Backtracking algorithm framework */
fn backtrack(state: &mut State, choices: &Vec<Choice>, res: &mut Vec<State>) {
// Check if it's a solution
if is_solution(state) {
// Record the solution
record_solution(state, res);
// Stop searching
return;
}
// Iterate through all choices
for choice in choices {
// Prune: check if the choice is valid
if is_valid(state, choice) {
// Trial: make a choice, update the state
make_choice(state, choice);
backtrack(state, choices, res);
// Retreat: undo the choice, revert to the previous state
undo_choice(state, choice);
}
}
}
/* Backtracking algorithm framework */
void backtrack(State *state, Choice *choices, int numChoices, State *res, int numRes) {
// Check if it's a solution
if (isSolution(state)) {
// Record the solution
recordSolution(state, res, numRes);
// Stop searching
return;
}
// Iterate through all choices
for (int i = 0; i < numChoices; i++) {
// Prune: check if the choice is valid
if (isValid(state, &choices[i])) {
// Trial: make a choice, update the state
makeChoice(state, &choices[i]);
backtrack(state, choices, numChoices, res, numRes);
// Retreat: undo the choice, revert to the previous state
undoChoice(state, &choices[i]);
}
}
}
/* Backtracking algorithm framework */
fun backtrack(state: State?, choices: List<Choice?>, res: List<State?>?) {
// Check if it's a solution
if (isSolution(state)) {
// Record the solution
recordSolution(state, res)
// Stop searching
return
}
// Iterate through all choices
for (choice in choices) {
// Prune: check if the choice is valid
if (isValid(state, choice)) {
// Trial: make a choice, update the state
makeChoice(state, choice)
backtrack(state, choices, res)
// Retreat: undo the choice, revert to the previous state
undoChoice(state, choice)
}
}
}
Now, we are able to solve Example Three using the framework code. The state is the node traversal path, choices are the current node's left and right children, and the result res is the list of paths:
As per the requirements, after finding a node with a value of \(7\), the search should continue. As a result, the return statement after recording the solution should be removed. The figure below compares the search processes with and without retaining the return statement.
Compared to the implementation based on pre-order traversal, the code using the backtracking algorithm framework seems verbose. However, it has better universality. In fact, many backtracking problems can be solved within this framework. We just need to define state and choices according to the specific problem and implement the methods in the framework.
Common terminology¶
To analyze algorithmic problems more clearly, we summarize the meanings of commonly used terminology in backtracking algorithms and provide corresponding examples from Example Three as shown in the table below.
Table
| Term | Definition | Example Three |
|---|---|---|
| Solution | A solution is an answer that satisfies specific conditions of the problem, which may have one or more | All paths from the root node to node \(7\) that meet the constraint |
| Constraint | Constraints are conditions in the problem that limit the feasibility of solutions, often used for pruning | Paths do not contain node \(3\) |
| State | State represents the situation of the problem at a certain moment, including choices made | Current visited node path, i.e., path node list |
| Trial | A trial is the process of exploring the solution space based on available choices, including making choices, updating the state, and checking if it's a solution | Recursively visiting left (right) child nodes, adding nodes to path, checking if the node's value is \(7\) |
| Retreat | Retreat refers to the action of undoing previous choices and returning to the previous state when encountering states that do not meet the constraints | When passing leaf nodes, ending node visits, encountering nodes with a value of \(3\), terminating the search, and the recursion function returns |
| Prune | Prune is a method to avoid meaningless search paths based on the characteristics and constraints of the problem, which can enhance search efficiency | When encountering a node with a value of \(3\), no further search is required |
Tip
Concepts like problems, solutions, states, etc., are universal, and are involved in divide and conquer, backtracking, dynamic programming, and greedy algorithms, among others.
Advantages and limitations¶
The backtracking algorithm is essentially a depth-first search algorithm that attempts all possible solutions until a satisfying solution is found. The advantage of this method is that it can find all possible solutions, and with reasonable pruning operations, it can be highly efficient.
However, when dealing with large-scale or complex problems, the running efficiency of backtracking algorithm may not be acceptable.
- Time complexity: Backtracking algorithms usually need to traverse all possible states in the state space, which can reach exponential or factorial time complexity.
- Space complexity: In recursive calls, it is necessary to save the current state (such as paths, auxiliary variables for pruning, etc.). When the depth is very large, the space need may become significantly bigger.
Even so, backtracking remains the best solution for certain search problems and constraint satisfaction problems. For these problems, there is no way to predict which choices can generate valid solutions. We have to traverse all possible choices. In this case, the key is about how to optimize the efficiency. There are two common efficiency optimization methods.
- Prune: Avoid searching paths that definitely will not produce a solution, thus saving time and space.
- Heuristic search: Introduce some strategies or estimates during the search process to prioritize the paths that are most likely to produce valid solutions.
Typical backtracking problems¶
Backtracking algorithms can be used to solve many search problems, constraint satisfaction problems, and combinatorial optimization problems.
Search problems: The goal of these problems is to find solutions that meet specific conditions.
- Full permutation problem: Given a set, find all possible permutations and combinations of it.
- Subset sum problem: Given a set and a target sum, find all subsets of the set that sum to the target.
- Tower of Hanoi problem: Given three rods and a series of different-sized discs, the goal is to move all the discs from one rod to another, moving only one disc at a time, and never placing a larger disc on a smaller one.
Constraint satisfaction problems: The goal of these problems is to find solutions that satisfy all the constraints.
- \(n\) queens: Place \(n\) queens on an \(n \times n\) chessboard so that they do not attack each other.
- Sudoku: Fill a \(9 \times 9\) grid with the numbers \(1\) to \(9\), ensuring that the numbers do not repeat in each row, each column, and each \(3 \times 3\) subgrid.
- Graph coloring problem: Given an undirected graph, color each vertex with the fewest possible colors so that adjacent vertices have different colors.
Combinatorial optimization problems: The goal of these problems is to find the optimal solution within a combination space that meets certain conditions.
- 0-1 knapsack problem: Given a set of items and a backpack, each item has a certain value and weight. The goal is to choose items to maximize the total value within the backpack's capacity limit.
- Traveling salesman problem: In a graph, starting from one point, visit all other points exactly once and then return to the starting point, seeking the shortest path.
- Maximum clique problem: Given an undirected graph, find the largest complete subgraph, i.e., a subgraph where any two vertices are connected by an edge.
Please note that for many combinatorial optimization problems, backtracking is not the optimal solution.
- The 0-1 knapsack problem is usually solved using dynamic programming to achieve higher time efficiency.
- The traveling salesman is a well-known NP-Hard problem, commonly solved using genetic algorithms and ant colony algorithms, among others.
- The maximum clique problem is a classic problem in graph theory, which can be solved using greedy algorithms and other heuristic methods.