CS计算机代考程序代写 discrete mathematics algorithm Math 154: Discrete Mathematics and Graph Theory

Math 154: Discrete Mathematics and Graph Theory

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Final Exam March 19th

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How do you deal with NP-Hard problems?

Local Search
Many optimization problems have a structure where solutions “nearby” a good solution will likely also be good.
This leads to a natural algorithmic idea:
Find an OK solution
Search nearby for better solutions
Repeat

Local Search
LocalSearch(f)
\\ Try to maximize f(x)
x ← Random initial point
Try all y close to x
If f(y) > f(x) for some y
x ← y
Repeat
Else Return x

Today
Local Search
Approximation Algorithms

MAXCUT
Problem: Given a graph G find a way to color the vertices of G black and white so that as many edges as possible have endpoints of different colors.

This is NP-Hard.

Question: MAXCUT
What is the size of the MAXCUT of the graph below?
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MAXCUT: Local Search
If possible recolor one vertex at a time for maximum improvement.

Problem
Stuck!

Local Maxima
Maximum
Local
Maximum

How to Get Unstuck
Randomized Restart
If you try many starting points, hopefully, you will find one that finds you the true maximum.
Expand Search Area
Look for changes to 2 or 3 vertices rather than 1.
Larger area means harder to get stuck
Larger area also takes more work per step
Still no guarantee of finding the actual maximum in polynomial time.

Simulated Annealing
At the start of algorithm take big random steps.
Hopefully, this will get you onto the right “hill”.
As the algorithm progresses, the “temperature” decreases and the algorithm starts to fine tune more precisely.
Works well in practice on a number of problems.

MAXCUT Minimal Value
Look back at local search for MAXCUT.
Swap a vertex if most of its neighbors are the same color.
At the end of the algorithm most of a vertices neighbors are the opposite color.
At the end of the algorithm at least half of the edges are cut.
Get cut of size at least |E|/2, but optimum at most |E|.

Approximation Algorithms
An α-approximation algorithm to an optimization problem is a (generally polynomial time) algorithm that is guaranteed to produce a solution within an α-factor of the best solution.
Our local search algorithm for MAXCUT is a 2-approximation algorithm.
Often approximation algorithms can produce good enough solutions.

Vertex Cover
Often greedy algorithms can give approximation algorithms.
Problem (Vertex Cover): Given a graph G find a set S of vertices so that every edge of G contains a vertex of S and so that |S| is as small as possible.

Also, NP-Hard.

Question Vertex Cover Example
What is the size of the smallest vertex cover in the graph below?
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Greedy Algorithm
GreedyVertexCover(G)
S ← {}
While(S doesn’t cover G)
(u,v) ← some uncovered edge
Add u and v to S
Return S

Simple and fast.

Example

Analysis
Algorithm finds k edges and 2k vertices.
Edges are vertex-disjoint.
Any cover must have at least one vertex on each of these edges.
Optimum cover has size at least k.
We have a 2-approximation.

Knapsack
Even though general knapsack is NP-Hard, we have a polynomial time algorithm if all weights are small integers (or more generally small integer multiples of some common value).
Since everything can be rounded to small integers, we have an algorithm idea.

Small Values
Actually rounding the weights doesn’t quite work. It gives you sets which almost fit in the sack.

Instead, we want to round the values of the items and for this, we need a new algorithm.

Dynamic Program
Let Lightest≤k(V) be the weight of the lightest collection of the first k items with total value V.
We have a recursion
Lightest≤k(V) =
min{Lightest≤k-1(V),Wt(k)+ Lightest≤k-1(V-Val(k))}
This gives a DP.
#subprobs = [Total Value][#items]
Time/Subproblem = O(1).

Approximation Algorithm
Throw away items that don’t fit in sac.
Let V0 be highest value of item.
Round each item’s value to closest multiple of δV0.
Run the small integer values DP.
Runtime: Values integer multiples of δV0. Total value at most [#items]V0 = ([#items]/δ) δV0.
Total runtime O([#items]2/δ).

Approximation Analysis
Optimal value at least V0.
Rounding changes the value of any set of items by at most [#items]δV0.
The solution we find is at least as good as the optimal after round.
Our solution is within [#items]δV0 of optimal.

Combining
Let δ = ε/[#items].
OPT ≥ V0.
Our solution is at most εV0 worse.
Have a (1+ε)-approximation algorithm.
Runtime = O([#items]3/ε)
For any ε > 0, have a (1+ε)-approximation in polynomial time. (known as a PTAS).