[SOLVED] 代写 C data structure algorithm graph =============================================================================

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CSC 263 Lecture Summary for Week 11 Fall 2019
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READING: Chapter 23.
SELF-TEST: Exercise 23.1-1, 23.2-2.

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Minimum Spanning Trees
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– Input: Connected, undirected, weighted graph G=(V,E) with weights w(e)
for each edge e in E.
Output: Find a spanning tree of G with smallest total weight.

– Spanning tree? Connected, acyclic subgraph of G — subset of edges that
is connected (at least one path between any two vertices) and acyclic (no
simple cycle). If G not connected, generalize to minimum spanning forest.

– Applications: circuit wiring, road paving, etc.

– Algorithm idea 1: Start with T = E; remove edges until MST remains.
Q: Which edges to remove?
A: Edges with largest weight whose endpoints are already connected —
in other words, the edge belongs to some cycle.
Possible but, in practice, inefficient.
. Fact: every tree on n vertices contains exactly n-1 edges.
. Fact: the maximum number of edges for an undirected graph on n
vertices is (n choose 2) = n(n-1)/2.
. So the algorithm may have to remove Omega(n^2) many edges to reach a
spanning tree.

– Algorithm idea 2: Start with T = {}; add edges until MST is reached —
generic “greedy” algorithm:

GREEDY-MST(G=(V,E),w:E->R)
T <- {}# invariant: T subset of some MST of Gwhile T is not a spanning tree:find e “safe for T”T <- T u {e}Loop condition? Test |T| = n-1 (where n = |V|) — since T is always asubset of some MST, by the loop invariant.”Safe” edges? Edge e is “safe for T” iff T u {e} is a subset of some MSTof G. This preserves loop invariant and makes algorithm triviallycorrect, but poses one major difficulty: how to find safe edgesefficiently? With no other information, current algorithm is somewhatcircular: to find a MST, you need to know which edges belong to someMST…- Theorem: If G is a connected, undirected, weighted graph, T is a subsetof some MST of G, and e is any edge _of_minimum_weight_ whose endpointsare in different connected components of T, then e is safe for T(T u {e} is a subset of some MST of G).Proof (in textbook) uses “exchange argument” — you will learn moreabout these types of proofs in CSC373.Prim’s algorithm:- Idea: pick a root (any vertex r in V will do) and “grow” MST byconnecting one isolated vertex at a time. [Picture: partial tree Trooted at r, isolated vertices outside T.] At each step, select edgewith minimum weight from T to any isolated vertex.How to make this efficient? Priority queue holds isolated vertices v,priority[v] = minimum weight of any edge to T (oo if no such edge),pi[v] = parent of v for edge of weight priority[v] (NIL if no such%%%%%%%edge).PRIM-MST(G=(V,E),w:E->R):
T <- {}Q <- empty priority queuefor all v in V:priority[v] <- oopi[v] <- NILEnqueue(Q,v)select vertex r in Vpriority[r] <- 0Update-Priority(Q,r)while Q is not empty:u <- ExtractMin(Q)T <- T u {(pi[u],u)}# problem when u = r: see belowfor each v in adj[u]:if v in Q and w(u,v) < priority[v]:priority[v] <- w(u,v)Update-Priority(Q,v)pi[v] <- uT <- T – {(NIL,r)}# “clean up”- Note: Update-Priority(Q,v) called “Decrease-Key” in textbook. Assumesconstant-time access to v’s position in Q — can be achieved bymaintaining additional field pos[v] for each v (set to -1 when v not inQ anymore, which also makes test “v in Q” efficient).- Trace (assuming root = C): Graph priority,parent of v in QT E B E DC B |2 | oo,NILoo,NIL0,NILoo,NIL{}1| |3 2,C 2,C3,C{} ||1,E3,C{{C,E}} D—C 3,C{{C,E},{E,D}} 2{{C,E},{E,D},{C,B}}Resulting MST: {{C,E},{E,D},{C,B}}.- Running Time? All priority queue operations take time Theta(log n). Atmost n ExtractMins performed. For each vertex, adjacency list examinedonce; for each edge, perform at most one Update-Priority. Total isO((n + m) log n) = O(m log n).Possible to do better by using more complex data structure for priorityqueue: “Fibonacci heaps” yield overall time O(m + n log n).Kruskal’s algorithm:- Idea: look at edges in order of non-decreasing weight, select each edgethat does not create a cycle in T.KRUSKAL-MST(G=(V,E),w:E->R):
T <- {}sort edges so w(e_1) <= w(e_2) <= … <= w(e_m)for i <- 1 to m:# let (u_i,v_i) = e_iif u_i,v_i in different connected components of T:T <- T u {e_i}- Difficulty: testing for components could be “expensive”.If we use BFS/DFS for this purpose, each test for connectivity takestime O(n). The loop iterates O(m) times, for a total of O(mn). This isin addition to the time to sort: O(m log m). Grand total = O(mn).- Use disjoint set ADT to keep track of connected components.KRUSKAL-MST(G=(V,E),w:E->R)
T <- {}sort edges so w(e_1) <= w(e_2) <= … <= w(e_m)for each v in V:Make-Set(v)for i <- 1 to m:# let (u_i,v_i) = e_iif Find-Set(u_i) != Find-Set(v_i):Union(u_i,v_i)T <- T u {e_i}Trace (assume edge {E,C} gets sorted before edge {D,C}): GraphDisjoint Sets T E B {E} {D} {C} {B}{} |2 | {E,D} {C} {B}{{E,D}}1| |3{E,D,C} {B}{{E,D},{E,C}} || {E,D,C} {B}{{E,D},{E,C}} D—C {E,D,C,B}{{E,D},{E,C},{B,C}} 2Resulting MST: {{E,D},{E,C},{B,C}}.Running time? O(m log m) for sorting weights, and O(m * T(n,m)) for mainloop, where T(n,m) is worst-case time for disjoint set operations.