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Today’s topics

Today’s topics. Decision Trees Reading: Sections 9.1. A Tree:. A Forest:. Tree and Forest Examples. Leaves in green, internal nodes in brown. Coin-Weighing Problem. Imagine you have 8 coins, one of which is a lighter counterfeit, and a free-beam balance.

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Today’s topics

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  1. Today’s topics • Decision Trees • Reading: Sections 9.1

  2. A Tree: A Forest: Tree and Forest Examples Leaves in green, internal nodes in brown.

  3. Coin-Weighing Problem • Imagine you have 8 coins, oneof which is a lighter counterfeit, and a free-beam balance. • No scale of weight markings is required for this problem! • How many weighings are needed to guarantee that the counterfeit coin will be found? ?

  4. As a Decision-Tree Problem • In each situation, we pick two disjoint and equal-size subsets of coins to put on the scale. A given sequence ofweighings thus yieldsa decision tree withbranching factor 3. The balance then“decides” whether to tip left, tip right, or stay balanced.

  5. Tree Height Theorem • The decision tree must have at least 8 leaf nodes, since there are 8 possible outcomes. • In terms of which coin is the counterfeit one. • Theorem: There are at most mh leaves in an m-ary tree of height h. • Corollary: An m-ary tree with  leaves has height h≥logm . If m is full and balanced then h=logm. • What is this decision tree’s height? • How many weighings will be necessary?

  6. General Solution Strategy • The problem is an example of searching for 1 unique particular item, from among a list of n otherwise identical items. • Somewhat analogous to the adage of “searching for a needle in haystack.” • Armed with our balance, we can attack the problem using a divide-and-conquer strategy, like what’s done in binary search. • We want to narrow down the set of possible locations where the desired item (coin) could be found down from n to just 1, in a logarithmic fashion. • Each weighing has 3 possible outcomes. • Thus, we should use it to partition the search space into 3 pieces that are as close to equal-sized as possible. • This strategy will lead to the minimum possible worst-case number of weighings required.

  7. General Balance Strategy • On each step, put n/3 of the n coins to be searched on each side of the scale. • If the scale tips to the left, then: • The lightweight fake is in the right set of n/3≈ n/3 coins. • If the scale tips to the right, then: • The lightweight fake is in the left set of n/3≈ n/3 coins. • If the scale stays balanced, then: • The fake is in the remaining set of n− 2n/3 ≈ n/3 coins that were not weighed! • Except if n mod 3 = 1 then we can do a little better by weighing n/3 of the coins on each side. You can prove that this strategy always leads to a balanced 3-ary tree.

  8. Coin Balancing Decision Tree • Here’s what the tree looks like in our case: 123 vs 456 left: 123 balanced:78 right: 456 4 vs. 5 1 vs. 2 7 vs. 8 L:1 L:4 L:7 R:2 B:3 R:5 B:6 R:8

  9. Problem 1 • How many weighings of a balance scale are needed to find a counterfeit coin among 12 coins if the counterfeit coin is lighter than the others? • Describe your algorithm to find this coin

  10. Problem 2 • 1 of 4 coins may be counterfeit • Counterfeit coin should be lighter or heavier than others • How many weighings are needed to • Determine whether there is a counterfeit coin • If so, is it lighter or heavier than others • What is your algorithm?

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