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BackTracking Algorithms Briana B. Morrison With thanks to Dr. Hung.

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Presentation on theme: "BackTracking Algorithms Briana B. Morrison With thanks to Dr. Hung."— Presentation transcript:

1 BackTracking Algorithms Briana B. Morrison With thanks to Dr. Hung

2 2 Topics What is Backtracking N-Queens Problem Sum of Subsets Graph Coloring Hamiltonian Circuits Other Problems

3 3 Algorithm Design Human Problems Result Input Data Structures Processing Output Data Structures Computer Algorithms

4 4 Algorithm Design … For a problem? What is an Optimal Solution? Minimum CPU time Minimum memory Example: Given 4 numbers, sort it to nonincreasing order. Method 1: Sequential comparison 1. Find the largest (3 comparisons) 2. Find the second largest (2 comparisons) 3. Find the third largest (1 comparisons) 4. Find the fourth largest A total of 6 comparisons

5 5 Algorithm Design … For a problem? What is an Optimal Solution? Minimum CPU time Minimum memory Example: Given 4 numbers, sort it to nonincreasing order. Method 2: Somewhat clever method a1 a2 a3 a4 a2a4 a2a3 a2a3 a2 a1a3 a3 or a1 (4 comparisons) (5 comparisons)

6 6 Backtracking Problems Find your way through the well-known maze of hedges by Hampton Court Palace in England? Until you reached a dead end. 0-1 Knapsack problem – exponential time complexity. N-Queens problem.

7 7 Backtracking Suppose you have to make a series of decisions, among various choices, where – You don’t have enough information to know what to choose – Each decision leads to a new set of choices – Some sequence of choices (possibly more than one) may be a solution to your problem Backtracking is a methodical way of trying out various sequences of decisions, until you find one that “works”

8 8 Introduction Backtracking is used to solve problems in which a sequence of objects is chosen from a specified set so that the sequence satisfies some criterion. Backtracking is a modified depth-first search of a tree. Backtracking involves only a tree search. Backtracking is the procedure whereby, after determining that a node can lead to nothing but dead nodes, we go back (“backtrack”) to the node’s parent and proceed with the search on the next child.

9 9 Introduction … We call a node nonpromising if when visiting the node we determine that it cannot possibly lead to a solution. Otherwise, we call it promising. In summary, backtracking consists of – Doing a depth-first search of a state space tree, – Checking whether each node is promising, and, if it is nonpromising, backtracking to the node’s parent. This is called pruning the state space tree, and the subtree consisting of the visited nodes is called the pruned state space tree.

10 10 Solving a maze Given a maze, find a path from start to finish At each intersection, you have to decide between three or fewer choices: – Go straight – Go left – Go right You don’t have enough information to choose correctly Each choice leads to another set of choices One or more sequences of choices may (or may not) lead to a solution Many types of maze problem can be solved with backtracking

11 11 Coloring a map You wish to color a map with not more than four colors – red, yellow, green, blue Adjacent countries must be in different colors You don’t have enough information to choose colors Each choice leads to another set of choices One or more sequences of choices may (or may not) lead to a solution Many coloring problems can be solved with backtracking

12 12 Solving a puzzle In this puzzle, all holes but one are filled with white pegs You can jump over one peg with another Jumped pegs are removed The object is to remove all but the last peg You don’t have enough information to jump correctly Each choice leads to another set of choices One or more sequences of choices may (or may not) lead to a solution Many kinds of puzzle can be solved with backtracking

13 13 Backtracking (animation) start? ? dead end ? ? ? success! dead end

14 14 N-Queens Problem Try to place N queens on an N * N board such that none of the queens can attack another queen. Remember that queens can move horizontally, vertically, or diagonally any distance. Let’s consider the 8 queen example…

15 15 The 8-Queens Example

16 16

17 17

18 18 Let’s look at it run

19 19 Terminology I There are three kinds of nodes: A tree is composed of nodes The (one) root node Internal nodes Leaf nodes Backtracking can be thought of as searching a tree for a particular “goal” leaf node

20 20 Terminology II Each non-leaf node in a tree is a parent of one or more other nodes (its children) Each node in the tree, other than the root, has exactly one parent parent children parent children Usually, however, we draw our trees downward, with the root at the top

21 21 Real and virtual trees There is a type of data structure called a tree – But we are not using it here If we diagram the sequence of choices we make, the diagram looks like a tree – In fact, we did just this a couple of slides ago – Our backtracking algorithm “sweeps out a tree” in “problem space”

22 22 The backtracking algorithm Backtracking is really quite simple--we “explore” each node, as follows: To “explore” node N: 1. If N is a goal node, return “success” 2. If N is a leaf node, return “failure” 3. For each child C of N, 3.1. Explore C If C was successful, return “success” 4. Return “failure”

23 23 Sum-of-Subsets problem Recall the thief and the 0-1 Knapsack problem. The goal is to maximize the total value of the stolen items while not making the total weight exceed W. If we sort the weights in nondecreasing order before doing the search, there is an obvious sign telling us that a node is nonpromising.

24 24 Sum-of-Subsets problem … Let total be the total weight of the remaining weights, a node at the ith level is nonpromising if weight + total > W

25 25 Example Say that our weight values are 5, 3, 2, 4, 1 W is 8 We could have – – – We want to find a sequence of values that satisfies the criteria of adding up to W

26 26 Tree Space Visualize a tree in which the children of the root indicate whether or not value has been picked (left is picked, right is not picked) Sort the values in non-decreasing order so the lightest value left is next on list Weight is the sum of the weights that have been included at level i Let weight be the sum of the weights that have been included up to a node at level i. Then, a node at the ith level is nonpromising if weight + w i+1 > W

27 27 Sum-of-Subsets problem … Example: Show the pruned state space tree when backtracking is used with n = 4, W = 13, and w 1 = 3, w 2 = 4, w 3 = 5, and w 4 = 6. Identify those nonpromising nodes.

28 28 Full example: Map coloring The Four Color Theorem states that any map on a plane can be colored with no more than four colors, so that no two countries with a common border are the same color For most maps, finding a legal coloring is easy For some maps, it can be fairly difficult to find a legal coloring We will develop a complete Java program to solve this problem

29 29 Data structures We need a data structure that is easy to work with, and supports: – Setting a color for each country – For each country, finding all adjacent countries We can do this with two arrays – An array of “colors”, where countryColor[i] is the color of the i th country – A ragged array of adjacent countries, where map[i][j] is the j th country adjacent to country i Example: map[5][3]==8 means the 3 th country adjacent to country 5 is country 8

30 30 Creating the map int map[][]; void createMap() { map = new int[7][]; map[0] = new int[] { 1, 4, 2, 5 }; map[1] = new int[] { 0, 4, 6, 5 }; map[2] = new int[] { 0, 4, 3, 6, 5 }; map[3] = new int[] { 2, 4, 6 }; map[4] = new int[] { 0, 1, 6, 3, 2 }; map[5] = new int[] { 2, 6, 1, 0 }; map[6] = new int[] { 2, 3, 4, 1, 5 }; }

31 31 Setting the initial colors static final int NONE = 0; static final int RED = 1; static final int YELLOW = 2; static final int GREEN = 3; static final int BLUE = 4; int mapColors[] = { NONE, NONE, NONE, NONE, NONE, NONE, NONE };

32 32 The main program ( The name of the enclosing class is ColoredMap ) public static void main(String args[]) { ColoredMap m = new ColoredMap(); m.createMap(); boolean result = m.explore(0, RED); System.out.println(result); m.printMap(); }

33 33 The backtracking method boolean explore(int country, int color) { if (country >= map.length) return true; if (okToColor(country, color)) { mapColors[country] = color; for (int i = RED; i <= BLUE; i++) { if (explore(country + 1, i)) return true; } return false; }

34 34 Checking if a color can be used boolean okToColor(int country, int color) { for (int i = 0; i < map[country].length; i++) { int ithAdjCountry = map[country][i]; if (mapColors[ithAdjCountry] == color) { return false; } return true; }

35 35 Printing the results void printMap() { for (int i = 0; i < mapColors.length; i++) { System.out.print("map[" + i + "] is "); switch (mapColors[i]) { case NONE: System.out.println("none"); break; case RED: System.out.println("red"); break; case YELLOW: System.out.println("yellow"); break; case GREEN: System.out.println("green"); break; case BLUE: System.out.println("blue"); break; } } }

36 36 Recap We went through all the countries recursively, starting with country zero At each country we had to decide a color – It had to be different from all adjacent countries – If we could not find a legal color, we reported failure – If we could find a color, we used it and recurred with the next country – If we ran out of countries (colored them all), we reported success When we returned from the topmost call, we were done

37 37 Hamiltonian Circuits Problem Hamiltonian circuit (tour) of a graph is a path that starts at a given vertex, visits each vertex in the graph exactly once, and ends at the starting vertex.

38 38 State Space Tree Put the starting vertex at level 0 in the tree At level 1, create a child node for the root node for each remaining vertex that is adjacent to the first vertex. At each node in level 2, create a child node for each of the adjacent vertices that are not in the path from the root to this vertex, and so on.

39 39 Example

40 40 Backtracking Algorithms The tic-tac-toe game xx How can a computer play the game? Remember Deep Blue?

41 41 Backtracking Algorithms The tic-tac-toe game x x (1,1)C (0,0)H (0,1)H (0,2)H (1,0)H... (0,0)C, (0,1)C, (1,0)C... (0,1)H, (1,0)H, …, (2,2)H (0,1)C, (1,0)C, (1,2)C, (2,0)C... : Computer x : Human

42 42 Backtracking Algorithms 3 missionaries and 2 cannibals want to cross the river Condition: 1. A boat can take one or two (must include a missionary) 2. At any time, on either bank, the number of missionaries must not be less than the number of cannibals.

43 43 Backtracking Search Essentially a simplified depth-first algorithm using recursion

44 44 Backtracking Search (3 variables) Assignment = {}

45 45 Backtracking Search (3 variables) Assignment = {(X 1,v 11 )} X1X1 v 11

46 46 Backtracking Search (3 variables) Assignment = {(X 1,v 11 ), (X 3,v 31 )} X1X1 v 11 v 31 X3X3

47 47 Backtracking Search (3 variables) Assignment = {(X 1,v 11 ), (X 3,v 31 )} X1X1 v 11 v 31 X3X3 X2X2 Assume that no value of X 2 leads to a valid assignment Then, the search algorithm backtracks to the previous variable and tries another value

48 48 Backtracking Search (3 variables) Assignment = {(X 1,v 11 ), (X 3,v 32 )} X1X1 v 11 X3X3 v 32 v 31 X2X2

49 49 Backtracking Search (3 variables) Assignment = {(X 1,v 11 ), (X 3,v 32 )} X1X1 v 11 X3X3 v 32 X2X2 Assume again that no value of X 2 leads to a valid assignment The search algorithm backtracks to the previous variable (X 3 ) and tries another value. But assume that X 3 has only two possible values. The algorithm backtracks to X 1 v 31 X2X2

50 50 Backtracking Search (3 variables) Assignment = {(X 1,v 12 )} X1X1 v 11 X3X3 v 32 X2X2 v 31 X2X2 v 12

51 51 Backtracking Search (3 variables) Assignment = {(X 1,v 12 ), (X 2,v 21 )} X1X1 v 11 X3X3 v 32 X2X2 v 31 X2X2 v 12 v21v21 X2X2

52 52 Backtracking Search (3 variables) Assignment = {(X 1,v 12 ), (X 2,v 21 )} X1X1 v 11 X3X3 v 32 X2X2 v 31 X2X2 v 12 v21v21 X2X2 The algorithm need not consider the variables in the same order in this sub-tree as in the other

53 53 Backtracking Search (3 variables) Assignment = {(X 1,v 12 ), (X 2,v 21 ), (X 3,v 32 )} X1X1 v 11 X3X3 v 32 X2X2 v 31 X2X2 v 12 v21v21 X2X2 v 32 X3X3

54 54 Backtracking Search (3 variables) Assignment = {(X 1,v 12 ), (X 2,v 21 ), (X 3,v 32 )} X1X1 v 11 X3X3 v 32 X2X2 v 31 X2X2 v 12 v21v21 X2X2 v 32 X3X3 The algorithm need not consider the values of X 3 in the same order in this sub-tree

55 55 Backtracking Search (3 variables) Assignment = {(X 1,v 12 ), (X 2,v 21 ), (X 3,v 32 )} X1X1 v 11 X3X3 v 32 X2X2 v 31 X2X2 v 12 v21v21 X2X2 v 32 X3X3 Since there are only three variables, the assignment is complete

56 56 Backtracking Algorithm CSP-BACKTRACKING(A) 1. If assignment A is complete then return A 2. X  select a variable not in A 3. D  select an ordering on the domain of X 4. For each value v in D do a. Add (X  v) to A b. If A is valid then i. result  CSP-BACKTRACKING(A) ii. If result  failure then return result 5. Return failure Call CSP-BACKTRACKING({}) [This recursive algorithm keeps too much data in memory. An iterative version could save memory (left as an exercise)]

57 57 Map Coloring {} WA=redWA=greenWA=blue WA=red NT=green WA=red NT=blue WA=red NT=green Q=red WA=red NT=green Q=blue WA NT SA Q NSW V T

58 58 Chapter Summary  Backtracking is an algorithm design technique for solving problems in which the number of choices grows at least exponentially with their instant size.  This approach makes it possible to solve many large instances of NP-hard problems in an acceptable amount of time.  The technique constructs a pruned state space tree.  Backtracking constructs its state-space tree in the depth-first search fashion in the majority of its applications.


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