Pivot partitioning by scanning: Difference between revisions
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'''Type of algorithm:''' loop | '''Type of algorithm:''' loop | ||
'''Auxiliary data:''' Five index pointers, <math>m_1,m_2,i_1,i_2,i_3 \in \ | '''Auxiliary data:''' Five index pointers, <math>m_1,m_2,i_1,i_2,i_3 \in \mathbb N.</math> | ||
'''Auxilliary data:''' | '''Auxilliary data:''' |
Revision as of 15:08, 13 January 2015
Algorithmic problem: Pivot partioning by scanning
Prerequisites: None
Type of algorithm: loop
Auxiliary data: Five index pointers, [math]\displaystyle{ m_1,m_2,i_1,i_2,i_3 \in \mathbb N. }[/math]
Auxilliary data:
- A constant representing the number of node: [math]\displaystyle{ n = |V| }[/math]
- A distance-valued [math]\displaystyle{ (n \times n) }[/math] matrix [math]\displaystyle{ M }[/math]
- C An ordering of all nodes, that is, [math]\displaystyle{ V = \{u_1, \ldots ,u_n\} }[/math]
Abstract View
Invariant: After [math]\displaystyle{ i \ge 0 }[/math] iterations, for [math]\displaystyle{ v,w \in V }[/math], [math]\displaystyle{ M(v,w) }[/math] is the length of a shortest [math]\displaystyle{ (v,w) }[/math]-path subject to the constraint that all internal nodes of the path belong to [math]\displaystyle{ \{u_1, \ldots , u_i\} }[/math].
Varian: [math]\displaystyle{ i }[/math] increases by [math]\displaystyle{ 1 }[/math].
Break condition: It is [math]\displaystyle{ i=n }[/math]
Induction basis
Abstract view: [math]\displaystyle{ \forall v,w \in V }[/math] we set
- [math]\displaystyle{ M(v,v):=0, \forall v \in V }[/math]
- [math]\displaystyle{ M(v,w):=l(v,w), (v,w) \in A }[/math]
- [math]\displaystyle{ M(v,w):= +\infty }[/math], otherwise
Implementation: Obvious.
Proof: Nothing to show.
Induction step
Abstract view:
Implementation:
Correctness: Let [math]\displaystyle{ p }[/math] denote a shoortest [math]\displaystyle{ (v,w }[/math]-path subject to the constraint that all internal nodes are taken from [math]\displaystyle{ \{u_1, \ldots, u_n \} }[/math].
- If [math]\displaystyle{ p }[/math] does not contain [math]\displaystyle{ u_i }[/math], [math]\displaystyle{ p }[/math] is even a shortest [math]\displaystyle{ (v,w) }[/math]-path such that all internal nodes are taken from [math]\displaystyle{ \{u_1, \ldots , u_{i-1} \} }[/math]. In this case, the induction hypothesis implies that the value of [math]\displaystyle{ M(v,w) }[/math] immediately before the i-th iteration equals the length of [math]\displaystyle{ p }[/math]. Clearly, the i-th iteration does not change the value of [math]\displaystyle{ M(v,w) }[/math] in this case.
- On the other hand, suppose [math]\displaystyle{ p }[/math] does contain [math]\displaystyle{ u_i }[/math]. Due to the prefix property, the segment of [math]\displaystyle{ p }[/math] from [math]\displaystyle{ v }[/math] to [math]\displaystyle{ u_i }[/math] and from [math]\displaystyle{ u_i }[/math] to [math]\displaystyle{ w }[/math] are a shortest [math]\displaystyle{ (v,u_i) }[/math]-path and a shortest [math]\displaystyle{ (u_i,w) }[/math]-path, respectively, subject to the constraint that all internal nodes are from [math]\displaystyle{ \{u_1, \ldots , u_{i-1} \} }[/math]. The induction hypothesis implies that tehese lengths equal the values [math]\displaystyle{ M(v,u_i) }[/math] and [math]\displaystyle{ M(u_i, w) }[/math], respectively.
Complexity
Statement: [math]\displaystyle{ \mathcal{O}(n^3) }[/math]
Proof: The overall loop has [math]\displaystyle{ n }[/math] iterations. In each iteration, we update all [math]\displaystyle{ n^2 }[/math] matrix entities. Each update requires a constant number of steps.