1. Search

2. Outline Problem space/ State space Exhaustive search Backtracking
Depth-first search
Breadth-first search
Backtracking
Branch-and-bound
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3. Problem Space
or State Space

4. Problem Space General problem statement
Given a problem instance P,
find answer A=(a1, a2, …, an) such that
the criteria C(A, P) is satisfied.
Problem space of P is the set of all possible answers A.
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5. Example: Shortest path
Given a graph G=(V,E), and nodes u and v, find the shortest path between u and v.
General problem statement
Given a graph G and nodes u and v,
find the path (u, n1, n2, …, nk, v), and
(u, n1, n2,…, nk, v) is the shortest path between u and v.
Problem space
Set of all possible path between u and v.
{(u, n1, n2, …, nk, v)| ni is in V, for 1ik}.
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6. Example: 0/1 Knapsack
Given a set S of n objects, where the object i has value vi and weight wi , and a knapsack with weight capacity C, find the maximum of value of objects in S which can be put in the knapsack.
General problem statement
Given vi and wi , for 1  i  n ,
find the set K such that for each i in K, 1  i  n,
 vi is maximum while  wi  C.
iK iK
Problem space
Any subset of S.
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7. Example: n Queens
Given an nxn board, find the n squares in the board to put n queens so that no pair can attack.
General problem statement
Find (p1, p2, …, pn) where pi = (xi, yi) is a square on the board, where there is no pair (xi, yi) and (xj, yj) such that xi = xj or yi = yj.
Problem space
A set of any n positions on the board.
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8. Exhaustive Search

9. Exhaustive Search Generate every possible answer
Test each answer with the constraint to find the correct answer
Inefficient because the number of answers in the problem space can be exponential.
Examples:
Shortest path
n! paths to be considered, where n is the number of nodes.
0/1 Knapsack
2n selections, where n is the number of objects.
n Queens
n2!/n! (n2-n)!
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10. State-Space Tree Let (a1, a2, …, an) be a possible answer.
Suppose ai is either 0 or 1, for 1  i  nใ
(?, …, ?)
(0, ?, …, ?)
(1, ?, …, ?)
(0, 0, ?, …, ?)
(0, 1, ?, …, ?)
(1, 0,?, …, ?)
(1, 1, ?, …, ?)
(0,0,0, …, ?)
(0,0,1, …, ?)
(0,1,0, …, ?)
(0,1,1, …, ?)
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11. State-Space Tree: Shortest Path8
(1) 3 5 2 5 2
(1,2)
(1,3) 1 1 -6 -1
(1,2,3)
(1,2,4)
(1,3,4)
(1,3,5) 2 2 4
(1,2,3,4)
(1,2,3,5)
(1,2,4,5)
(1,3,4,5)
(1,2,3,4,5)
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12. Generating Possible Paths
Let {1,2,3, …, n} be a set of nodes and E[i][j] is the weight of the edge between node i and j.
path(p)
last = last node in the path p
for next = 1 to n
do np = p
if next is not in np and E[last][next] != 0
then np = np || next
path(np)
else return
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13. State-Space Tree : 0/1 Knapsack
Given a set of objects o1, …, o5.
{ }
{1}
{2}
{3}
{4}
{5}
{1,2}
{1,3}
{1,4}
{1,5}
{1,2,3}
{1,2,4}
{1,2,5}
{1,3,4}
{1,3,5}
{1,4,5}
{1,2,3,4}
{1,2,3,5}
{1,2,4,5}
{1,3,4, 5}
{1,2,3,4,5}
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14. State-Space Tree : n Queen
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15. Depth-first Search
Traverse the tree from root until a leaf is reached.
Then, traverse back up to visited the next unvisited node.
depthFirst(v)
visited[v] = 1
for each node k adjacent to v
do if not visited[k]
then depthFirst(k)
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16. 0/1 Knapsack: Depth-first Search
Global: maxV=0 maxSack={}
DFknapsack(sack, unchosen)
for each object p in unchosen
do unchosen=unchosen-{p}
sack=sack U {p}
val=evaluate(sack)
if unchosen is empty
► A leaf is reached.
then maxV=max(maxV, val)
if maxV=val then maxSack=sack
return
else DFknapsack(sack, unchosen)
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17. Breadth-first Search
Traverse the tree from root until the nodes of the same depth are all visited.
Then, visited the node in the next level.
breadthFirst(v)
Q = empty queue
enqueue(Q, v) visited[v] = 1
while not empty (Q)
do u = dequeue(Q)
for each node k adjacent to u
do if not visited[k]
then visited[k] = true
enqueue(Q, k)
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18. 0/1 Knapsack: Breadth-first Search
BFknapsack
Q = empty queue maxV=0
sack = { }
unchosen = set of all objects
enqueue(Q, <sack, unchosen>)
while not empty (Q)
do <sack, unchosen> = dequeue(Q)
if unchosen is not empty
then for each object p in unchosen
do enqueue(Q,<sackU{p}, unchosen-{p}>)
else maxV = max(maxV, evaluate(sack))
if maxV=evaluate(sack) then maxSack = sack
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19. Backtracking

20. Backtracking
Reduce the search by cutting down some branches in the tree
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21. 0/1 Knapsack: Backtracking{}
0,0
Sack
Current weight, current value
Node
{1}
2,5
{2}
1,4
{3}
3,8
{4}
2,7
{1,2}
3,9
{1,3}
5,13
{1,4}
4,12
{2,3}
4,12
{2,4}
3,11
{3,4}
5,15
object
weight
value 1 2 5 4 3 8 7
{2,3,4}
6,19
{1,2,3}
6, 17
{1,2,4}
5, 16
Capacity = 5
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22. 0/1 Knapsack: Backtracking
BTknapsack(sack, unchosen)
for each object p in unchosen
do unchosen=unchosen-{p}
if p can be put in sack then sack = sack U {p}
► Backtracking occurs when p cannot be put in sack.
val=evaluate(sack)
if unchosen is empty
► A leaf is reached.
then maxV=max(maxV, val)
maxSack=sack
return
else BTknapsack(sack, unchosen)
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23. Branch-and-Bound

24. Branch-and-bound Use for optimization problems
An optimal solution is a feasible solution with the optimal value of the objective function.
Search in state-space trees can be pruned by using bound.
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25. State-Space Tree with Bound
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26. Branch and Bound From a node N:
If the bound of N is not better than the current overall bound, terminate the search from N.
Otherwise,
If N is a leaf node
If its bound is better than the current overall bound, update the overall bound and terminate the search from N.
Otherwise, terminate the search from N.
Otherwise, search each child of N.
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27. 0/1 Knapsack: Branch-and-Bound
Global: OvBound=0
BBknapsack(sack, unchosen)
if bound(sack, unchosen)>OvBound
► Estimated bound can be better than the overall bound.
then for each object p in unchosen
do unchosen=unchosen-{p}
if p can be put in sack then sack = sack U {p}
► Backtracking occurs when p cannot be put in sack.
val=evaluate(sack)
if unchosen is empty
► A leaf is reached.
then maxV = max(maxV, val)
maxSack = sack
OvBound = max(evaluate(sack), OvBound)
return
else ► A leaf is not reached.
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28. 0/1 Knapsack: Estimated bound
Current value
available space * best ratio value:space of unchosen objects
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29. 0/1 Knapsack: Branch-and-bound
{} 20
0,0
Sack estimated bound
Current weight, current value
Node
{1}17
2,5
{2}18
1,4
{3}16
3,8
{4}19
2,7
{1,2}16
3,9
{1,3} 13
5,13
{2,3}15.5
4,12
{2,4}16.3
3,11
{3,4}15
5,15
object
weight
value
ratio 1 2 5
2.5 4 3 8
2.67 7
3.5
{1,2,4} 16
5, 16
Overall bound 16
Capacity = 5
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