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# Planar point location -- example - PowerPoint PPT Presentation

Planar point location -- example. Planar point location & persistence (cont). Updates should be persistent since we need all search trees at the end. Partial persistence is enough. Well, we already have the path copying method, lets use it. What do we get ?.

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Updates should be persistent since we need all search trees at the end.

Partial persistence is enough

Well, we already have the path copying method, lets use it.

What do we get ?

O(nlogn) space and O(nlog n) preprocessing time.

We shall improve the space bound to O(n).

Break each operation into elementary access steps (ptr traversal) and update steps (assignments, allocations).

Want a persistent simulation with consumes O(1) time per update or access step, and O(1) space per update step.

We will show a general technique to make data structures partially and later fully persistent.

The time penalty of the transformation would be O(1) per elementary access and update step.

The space penalty of the transformation would be O(1) per update step.

In particular, this would give us an O(n) space solution to the planar point location problem

Every pointer field can store many values, each tagged with a version number.

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Simulation of an update step when producing version i:

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• When a new node is created by the ephemeral update we create a new node, each value of a field in the new node is marked with version i.

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• When we change a value of a field f to v, we add an entry to the list of f with key i and value v

Simulation of an access step when navigating in version i:

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• The relevant value is the one tagged with the largest version number smaller than i

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V2 = inject(y,V1)

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V3 = eject(V2)

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V4= inject(z,V3)

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Partialy persistent deques via the fat node method

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Space is ok -- O(1) per update step

That would give O(n) space for planar point location since each insertion/deletion does O(1) changes amortized.

We screwed up the update time, it may take O(log m) to traverse a pointer, where m is the # of versions

So query time goes up to O(log2n) and preprocessing time is O(nlog2n)

This is a general method to make pointer based data structures partially persistent.

Nodes have to have bounded in degree and bounded outdegree

We will show this method first for balanced search trees which is a slightly simpler case than the general case.

Idea: It is similar to the fat node method just that we won’t make nodes too fat.

Here it suffices to allow one extra pointer field in each node

Each extra pointer is tagged with a version number and a field name.

When the ephemeral update allocates a new node you allocate a new node as well.

When the ephemeral update changes a pointer field if the extra pointer is empty use it, otherwise copy the node. Try to store pointer to the new copy in its parent.

If the extra ptr at the parent is occupied copy the parent and continue going up this way.

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The time slowdown per access step is O(1) since there is only a constant # of extra pointers per node.

What about the space blowup ?

O(1) (amortized) new nodes per update step due to nodes that would have been created by the ephemeral implementation as well.

How about nodes that are created due to node copying when the extra pointer is full ?

We’ll show that only O(1) of copings occur on the average per update step.

Amorized space consumption = real space consumption + 

 = #(used slots in live nodes)

A node is live if it is reachable from the root of the most recent version.

==> Amortized space cost of node copying is 0.

Each persistent nodes has d + p + e + 1 pointers

e = extra pointers

p = predecessor pointers

1 = copy pointer.

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live

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Simulating an update step in node x

When there is no free extra ptr in x copy x.

When you copy node x, and x points to y, c(x) should point to y, update the corresponding predecessor ptr in y. Add x to the set S of copied nodes.

(S contains no nodes initially)

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Node copying in general (cont)

Take out a node x from S, go to nodes pointing to x and update then, maybe copying more nodes

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Take out a node x from S, go to nodes pointing to x and update then, maybe copying more nodes

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Take out a node x from S, go to nodes pointing to x and update then, maybe copying more nodes

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Take out a node x from S, go to nodes pointing to x and update then, maybe copying more nodes

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• Remove any node x from S,

• for each node y indicated by a predecessor pointer in x

• find in y the live pointer to x.

• If this ptr has version stamp i, replace it by a ptr to c(x). Update the corresponding reverse pointer

• If this ptr has version stamp less than i, add to y a ptr to c(x) with version stamp i. If there is no room, copy y as before, and add it to S. Update the corresponding reverse pointer

Actual space consumed is |S|

 = #(used extra fields in live nodes)

 = -e|S| + p|S|

This is smaller than |S| if e > p (Actually e ≥ p suffices if we were more careful)

So whether there were any copings or not the amortized space cost of a single update step is O(1)

Does it also work for full persistence ?

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We have a navigation problem.

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The fat node method - full persistence (cont)

Maintain a total order of the version tree.

When a new version is created add it to the list immediately after its parent.

==> The list is a preorder of the version tree.

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The fat node method - full persistence (cont)

When traversing a field in version i, the relevant value is the one recorded with a version preceding i in the list and closest to it.

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The fat node method - full persistence (cont)

How do we update ?

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The fat node method - full persistence (cont)

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So what is the algorithm in general ?

Suppose that when we create version i we change field f to have value v.

Let i1 (i2) be the first version to the left (right) of i that has a value recorded at field f

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The fat node method - full persistence (cont)

We add the pair (i,v) to the list of f

Let i+ be the version following i in the version list

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If (i+ < i2) or i+ exists and i2 does not exist add the pair (i+,v’) where v’ is the value associated with i1.

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How do we efficiently find the right value of a field in version i ?

Store the values sorted by the order determined by the version list. Use a search tree to represent this sorted list.

To carry out a find on such a search tree we need in each node to answer an order query on the version list.

Use Dietz and Sleator’s data structure for the version list.

We can find the value to traverse in O(log(m)) where m is the number of versions

We get O(1) space increase per ephemeral update step

O(log m) time slowdown per ephemeral access step

Similar to node copying. (slightly more evolved)

Allows to avoid the O(log m) time slowdown.

Converts any pointer based data structure with constant indegrees and outdegrees to a fully persistent one.

The time slowdown per access step is O(1) (amortized).

The space blowup per update step is O(1) (amortized)

You get fully persistent search trees in which each operation takes O(log n) amortized time and space.

Why is the space O(log n) ?

Since in the ephemeral settings the space consumption is O(1) only amortized.

So what do we need in order to get persistent search trees with O(1) space cost per update (amortized) ?

We need an ephemeral structure in which the space consumption per update is O(1) on the worst case.

You can do it !

==> Red-black trees with lazy recoloring

We can apply node splitting to get fully persistent deques with O(1) time per operation.

We can also transform the simulation by stacks into a real time simulation and get O(1) time solution.

What if we want to add the operation concatenate ?

None of the methods seems to extend...