1. Lecture 2
Geometric Algorithms

2. Sedgewick Sample PointsB C D E F G H I J K L M N O P

3. Simple Closed Path (Anchor = I)B C D E F G H I J K L M N O P

4. Horizontal Sorting (X component)B C D E F G H I J K L M N O P

5. Vertical Sorting (Y component)B C D E F G H I J K L M N O P

6. Nearest Neighbor A B C D E F G H I J K L M N O P

7. Crossing Line Removal AlgorithmB C D E F G H I J K L M N O P

8. Convex Hull A B C D E F G H I J K L M N O P

9. Introduction
Sometimes we can find an exisiting algorithm that fits our problem, however,  it is more likely that we will have to modify a known algorithm to make it applicable to a specific application. 
Often we will have to develop an algorithm based on one or more of the general problem-solving methods covered in this course.
The Munkres' Assignment algorithm is a combination of
the Greedy Method (Step 1/Step 2),
a Verification of Optimality (Step 3)
Constraint Relaxation (Step 4/Step 6) and
the Augmenting Path (Backtracking) algorithm (Step 5).
Occasionally, even the general methods are not applicable and we are forced to develop a completely new algorithm to solve a problem.  In this section we will consider a number of problems best represnted as graphs, trees, or paths in graphs. We will refer to these as dot-and-line problems.

10. Minimal Route Problem
Given a set of points in the plane, find a minimal length path that connects the points.

11. Convex Hull
Given a set of points in the plane, find the subset of points that defines a convex boundary which contains all the points.

12. Polygon Triangulation
Given a set of points in the plane defining a convex polygon, find the chords with a minimal total length that triangulate the polygon

13. Track Correlation
Given a sequence of M snapshots of a set of N points in the plane, obtain the N lists of M points representing the same point in each of the M frames. Each
of the N points is assumed to be moving at a constant rate between frames

14. Euclidean Traveling Salesperson Problem
Given a set of points in the plane, find a minimal length tour of all the points.  This is a special case of the traveling salesperson problem.
It can be shown that the solution to the minimal routing problem is, in general, NOT a subset of the solution of this problem (find a counterexample).

15. Maximal Disjoint Paths Problem
Given an undirected graph as shown, find the maximum number of paths that connect a selected pair of nodes and share no edges. 
There is also a version of this problem in which the disjoint paths can have no common vertices (except the end vertices). 
In the figure on the right three edge-disjoint paths are shown for the red and green vertices.  The violet and orange paths are also vertex-disjoint.

16. Minimal Route Problem
The solution to this problem involves the arrangment of the n points so that the total distance between successive point pairs in the list is minimal. The brute-force approach to this problem is not practical for large n since there are n! arrangements of n points.  So how do we procede?
n=100 randomly ordered points in R2

17. Let'st try the greedy method first. 
Start with any point (this could be a point selected at random or we could choose the point based on some criterion such as the point that is most distant from its nearest neighbor). Make this the first point in the list. 
Choose the nearest-neighbor to the first point to be the second point in the list.  Now choose the nearest-neighbor (from the unselected set of points) of the second point as the third point in the list, and so on. 
At first this seems to be working quite well but as the number of unselected points diminishes the few remaining points are scattered around the point set distribution making the last few selections clearly non-optimal.

18. The Euclidean distance between any two points is computed by,
  function dist(a,b : point) return float is   begin     return sqrt((float(a.x)-float(b.x))**2+(float(a.y)-float(b.y))**2);   end dist;
We can build a simple nearest_neighbor sorting routine similar to exchange sort for scalar values.
  procedure nearest_neighbor is   begin     for i in 1..np-1 loop       for j in i+1..np loop         if dist(P(i),P(j))<dist(P(i),P(i+1)) then           swap(P(i+1),P(j));         end if;       end loop;     end loop;   end nearest_neighbor;

19. Given that the point set is P and the number of points is np
Given that the point set is P and the number of points is np.  Viewing the results of the nearest-neighbor routine shows an improved ordering but it is clear that there is room for improvement. 
We will use the graphical representation of the solution as a mechanism for improving our algorithm. As we have already noted, an algorithm that finds the minimal route is probably exponential in complexity. 
For most practical applications involving minimal routes a near-optimal solution is sufficient. We will try to build an optimal algorithm but we may succeed only in building a heuristic

20. For the moment, let's concentrate on a small portion of the the route obtained from the greedy method. 
Note that the path from b to c to d to e is clearly farther than the path from b to d to c to e.
We will let dist(x,y) represent the distance between point x and point y.  In this case we are saying that since dist(b,c) + dist(d,e) > dist(b,d) + dist(c,e) we will exchange the points c and d in our list of points.

21. For lack of a better name we will call this procedure switch_back.
How can be build this test into a search of the entire list?  First of all, we need to refer to the points in a more general way than a,b,c etc.  Since the points are in the list P we can make the following generalization:
for i in 1..np-3 loop   if dist(P(i),P(i+1))+dist(P(i+2),P(i+3)) >      dist(P(i),P(i+2))+dist(P(i+1),P(i+3)) then     swap(P(i+1),P(i+2));   end if; end loop;
For lack of a better name we will call this procedure switch_back.

22. Before Crossing-Line Removal

23. One Pass Crossing Line Removal

24. The areas in red boxes are those portions of the point set ordering that were changed by the switch_back procedure. 
The total length of the route is improved but we still have a number of crossing lines. 
Let's take a closer look at the crossings to see if we can further improve our algorithm.
By direct examination of the simplified example below, we know that there must be a shorter route when lines connecting the points are crossing.

25. This comparison is given by,
This is true since the sum of the diagonals of the quadrilateral defined by points i, j, i+1, j+1 is greater than the sum of the opposite sides.  This is true for any convex quadrilateral.
The crossing lines can be any two lines in the route, therefore we need to use two nested loops to test all line pairs.  In fact, we don't care if they are crossing, but rather if their sum exceeds the sum of the alternate pair of lines. 
This comparison is given by,
dist(P(i),P(i+1))+dist(P(j),P(j+1)) >
dist(P(i),P(j))+dist(P(i+1),P(j+1))

26. We must be careful in this case
We must be careful in this case.  If we decide make the exchange we must reverse all the points in the line segment from i+1 to j in our list of points.
for i in 1..n-3 loop   for j in i+2..n loop     if dist(P(i),P(i+1))+dist(P(j),P(j+1)) >        dist(P(i),P(j))+dist(P(i+1),P(j+1)) then       reverse(i+1,j);     end if;   end loop; end loop;

27. The Reverse( ) Procedure

28. Applying the Cross( ) Procedure
This procedure (call it cross) must be applied repeatedly since removal of one pair of crossed lines may create other crossing lines.  We will apply this algorithm until there is no further reduction in total route length. 
For each of these procedures we note that we are comparing all line segments that will be affected by the change, so that we will only make the change if it improves the situation.

29. Summary
In our example there was no further improvement after two applications of the cross( ) procedure.  Is this route minimal? 
Later, we will show that cross( ) is not an optimal algorithm but the application of nearest_neighbor and cross( ) makes an good heuristic for near-optimal solutions to the minimal route problem.
There are many other heuristics that can be applied to this problem. Since this is a sorting problem, consider methods analogous to the scalar sorts we already know, such as...
Insertion Sort
Exchange Sort
Bubble Sort