U N I V E R S I T Y O F B E R G E N

### Minicourse on parameterized algorithms and complexity

### Part 1: Basic techniques

**Dániel Marx**

**(slides by Bart M. P. Jansen)**
**November 2, 2016**

### Why we are here

• To create the recipes that make computers solve our problems
**efficiently**

– With a bounded number of resources (memory, time)

• We measure the quality of an algorithm by the dependence of its
**running time on the size of the input**

– For an -bit input, the running time can be

– Smaller functions are better, but as a general guideline:

• Polynomials are good, exponential functions are bad

• Unfortunately, many problems are NP-complete

• We believe that for NP-complete problems, there is no algorithm that:

– always gives the right answer, and whose

– running time is bounded by a polynomial function of the input size

•

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### Dealing with NP-complete problems

Approximation Sacrifice quality of the solution: quickly find a solution that is provably not
*very bad*

Local search Quickly find a solution for which you cannot give any quality guarantee (but which might often be good)

Branch &

bound

Sacrifice running time guarantees: create an algorithm for which you do not know how long it will take (but which might do well on the inputs you use)

**Parameterized **
**algorithms**

Sacrifice the running time: allow the running time to have an exponential factor, but ensure that the exponential dependence is not on the entire input size but just on some parameter that is hopefully small

**Kernelization** Quickly shrink the input by preprocessing so that afterward running an
exponential-time algorithm on the shrunk instance is fast enough

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**FIXED-PARAMETER TRACTABILITY**

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### Parameterized problems

• As usual in complexity theory, we primarily study decision
**problems (YES/NO questions)**

– OPTIMIZATION: “Find the shortest path from to ”

– DECISION: “Is there a path from to of length at most ?”

• Having an efficient algorithm for one typically gives an efficient algorithm for the other

• A parameterized problem is a decision problem where we associate an integer parameter to each instance

– The parameter measures some aspect of the instance

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### Problem parameterizations

• PACKET DELIVERY PROBLEM

**Input: A graph , a starting vertex , a set of delivery **
vertices, and an integer

**Question: Is there a cycle in that starts and ends in , **
visits all vertices in , and has length at most ?

• There are many possible parameters for this problem:

– The length of the tour

– The number of delivery points

– Graph-theoretic measures of how complex is (treewidth, cliquewidth, vertex cover number)

• Parameterized complexity investigates:

•

Can the problem be solved efficiently, if the parameter is small?

Can the problem be solved efficiently, if the parameter is small?

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### Fixed-parameter tractability – informally

• A parameterized problem is fixed-parameter tractable if there is an algorithm that solves size- inputs with parameter value in time for some constant and function

• *For each fixed , there is a polynomial-time algorithm*

• VERTEX COVER:

– “Can all the edges of this -vertex graph be covered by at most vertices?”

– Solvable in time , so FPT

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### Fixed-parameter tractability – formally

• Let be a finite alphabet used to encode inputs – ( for binary encodings)

• A parameterized problem is a set

• The set contains the tuples where the answer to the question encoded by is yes; is the parameter

• The parameterized problem is fixed-parameter tractable if there is an algorithm that, given an input ,

– decides if belongs to or not, and

– runs in time for some function and constant

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**KERNELIZATION**

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### Data reduction with a guarantee

• **Kernelization is a method for parameterized preprocessing**

– Efficiently reduce an instance to an equivalent instance of size bounded by some

• One of the simplest ways of obtaining FPT algorithms

– Apply a brute force algorithm on the shrunk instance to get an FPT algorithm

• Kernelization also allows a rigorous mathematical analysis of efficient preprocessing

•

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### The VERTEX COVER problem

**Input:** An undirected graph and an integer
**Parameter:**

**Question:** Is there a set of at most vertices in , such that
each edge of has an endpoint in ?

• Such a set S is a vertex cover of

•

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### Reduction rules for VERTEX COVER – (R1)

(R1) If there is an isolated vertex , delete from – Reduce to the instance

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### Reduction rules for VERTEX COVER – (R1)

(R1) If there is an isolated vertex , delete from – Reduce to the instance

• To ensure that a reduction rule does not change the answer, we have to prove safeness of the reduction rule

• If is transformed into then we should prove that:

** is a YES-instance is a YES-instance**

•

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### Reduction rules for VERTEX COVER – (R1)

(R1) If there is an isolated vertex , delete from – Reduce to the instance

•

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### Reduction rules for VERTEX COVER – (R2)

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(R2) If there is a vertex of degree more than , then delete (and its incident edges) from and decrease the parameter by 1

– Reduce to the instance

•

### Reduction rules for VERTEX COVER – (R3)

(R3) If the previous rules are not applicable and has more than vertices or more than edges, then conclude that we are dealing with a NO-instance

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### Correctness of the cutoff rule

• **Claim. If is exhaustively reduced under (R1)-(R2) and has more than **
vertices or edges, then there is no size- vertex cover

• **Proof. **

– Suppose has a vertex cover

– Since (R1) does not apply, every vertex of has at least one edge – Since (R2) does not apply, every vertex has degree at most :

– So

– So if has a size- vertex cover, and

•

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### SS

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### Preprocessing for VERTEX COVER

• (R1)-(R3) can be exhaustively applied in polynomial time

• In polynomial time, we can reduce a VERTEX COVER instance to an instance such that:

– the two instances are equivalent: has answer YES if and only if has answer YES

– instance has at most vertices and edges

• This gives an FPT algorithm to solve an instance : – Compute reduced instance

– Solve by brute force: try all vertex subsets

• For each , test if it is a vertex cover of size at most

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**Theorem. -VERTEX COVER is fixed-parameter tractable**

**Theorem. -VERTEX COVER is fixed-parameter tractable**

### Kernelization – formally

• Let be a parameterized problem and

• A kernelization (or kernel) for of size is an algorithm that, given , takes time polynomial in , and outputs an instance such that:

• A polynomial kernel is a kernel whose function is a polynomial

•

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**Theorem. A parameterized problem is fixed-**

parameter tractable if and only if it is decidable and has a kernel (of arbitrary size)

**Theorem. A parameterized problem is fixed-**

parameter tractable if and only if it is decidable and has a kernel (of arbitrary size)

### Kernel for FEEDBACK ARC SET IN TOURNAMENTS

**Input:** A tournament and an integer
**Parameter:**

**Question:** Is there a set of at most directed edges in , such
that is acyclic?

•

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### Reduction rules for FEEDBACK ARC SET

(R1) If vertex is not in any triangle, then remove

(R2) If edge is in at least distinct triangles, reverse it and decrease by one

(R3) If the previous rules are not applicable and has more than vertices, then conclude that we are dealing with a NO-instance

•

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**Theorem. -FEEDBACK ARC SET IN **

### TOURNAMENTS has a kernel with vertices **Theorem. -FEEDBACK ARC SET IN **

### TOURNAMENTS has a kernel with vertices

### High-level kernelization strategy

• Compare to VERTEX COVER:

– (R1) deals with elements that do not constrain the solution – (R2) deals with elements that must be in any solution

– (R3) deals with graphs that remain large after reduction

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**BOUNDED-DEPTH SEARCH TREES**

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### Background

• A branching algorithm that explores a search tree of

**bounded depth is one of the simplest types of FPT algorithms**

• Main idea:

– Reduce problem instance to solving a bounded number of instances with parameter

• If you can solve in polynomial time using the answers to two instances and , then the problem can be solved in time

– (assuming the case is polynomial-time solvable)

• If you generate subproblems instead of 2, then the problem can be solved in time

•

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### Analysis of bounded-depth search trees

• If the parameter decreases for each recursive call, the depth of the tree is at most

• # nodes in a depth- tree with leaves is

– Usually sufficient to bound the number of leaves

• If the computation in each node takes polynomial time, total running time is

•

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### VERTEX COVER revisited

**Input:** A graph and an integer
**Parameter:**

**Question:** Is there a set of at most vertices in , such that
each edge has an endpoint in ?

•

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### Algorithm for VERTEX COVER

• **Algorithm VC(Graph , integer )**

• **if then return NO**

• **if has no edges then return YES**

• **else pick an edge in and let and be its endpoints**
– return (VC OR (VC)

• Correct because any vertex cover must use or

• A size- vertex cover in G that uses , yields a size- vertex cover in

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### Running time for VERTEX COVER

• Every iteration either solves the problem directly or makes two recursive calls with a decreased parameter

• The branching factor of the algorithm–and therefore of the search tree–is two

• Tree of depth with branching factor has at most leaves – Running time is

– Much better than from the kernelization algorithm

• One way to faster algorithms:

– Pick a vertex of maximum degree, recurse on and

•

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### The FEEDBACK VERTEX SET problem

**Input:** An undirected (multi)graph and an integer
**Parameter:**

**Question:** Is there a set of at most vertices in , such that
each cycle contains a vertex of ?

• We allow multiple edges and self-loops

• Such a set is a feedback vertex set of

– Removing from results in an acyclic graph, a forest

•

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### Branching for FEEDBACK VERTEX SET

• For VERTEX COVER, we could easily identify a set of vertices to branch on: the two endpoints of an edge

• For feedback vertex set, a solution may not contain any endpoint of an edge

– How should we branch?

• We will find a set of vertices such that any size- feedback vertex set contains a vertex of

• To find we first have to simplify the graph using reduction rules that do not change the answer

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### Reduction rules

(R1) If there is a loop at vertex , then delete and decrease by one (R2) If there is an edge of multiplicity larger than , then reduce its multiplicity to

(R3) If there is a vertex of degree at most , then delete

(R4) If there is a vertex of degree two, then delete and add an edge between ’s neighbors

•

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If (R1-R4) cannot be applied anymore, then the minimum degree is at least If (R1-R4) cannot be applied anymore,

then the minimum degree is at least

**Observation. If is transformed into , then:**

1. FVS of size in FVS of size in

2. Any feedback vertex set in is a feedback vertex set in when combined with the vertices deleted by (R1)

**Observation. If is transformed into , then:**

1. FVS of size in FVS of size in

2. Any feedback vertex set in is a feedback vertex set in when combined with the vertices deleted by (R1)

### Identifying a set to branch on

• Let be a graph whose vertices have degree three or more – Order the vertices as by decreasing degree

– Let be the largest-degree vertices

• **Lemma. If all vertices of have degree 3 or more, then any **
size- feedback vertex set of contains a vertex from

• So if there is a size- solution, it contains a vertex of – For each recurse on the instance

• Gives an algorithm with running time

– Apply the reduction rules, compute , then branch

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### A useful claim

• **Claim. If is a feedback vertex set of , then**

• **Proof. Graph is a forest**
– So

– Every edge not in , is incident with a vertex of

• With this claim, we can prove the degree lemma

•

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### Proving the degree lemma

• **Lemma. If all vertices of have degree 3 or more, then any **
size- feedback vertex set of G contains a vertex from

• **Proof by contradiction. **

– Let be a size- feedback vertex set with – By choice of we have:

, so:

– Define . Since :

•

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### Proving the degree lemma (II)

• The degree sum counts every edge twice:

• Combining these:

• So

• But since all vertices have degree we have:

• Contradiction

•

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### A final word on bounded-depth search trees

• The degree lemma proves the correctness of our branching strategy for FEEDBACK VERTEX SET

• When building a branching algorithm for a parameterization by the solution size:

– Find an -size set that contains a vertex of the solution – Branch in directions, trying all possibilities

– We get a search tree of depth and branching factor

• You can think of the branching process as guessing

•

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**DYNAMIC PROGRAMMING**

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### The SET COVER problem

**Input:** A set family over a universe and an integer
**Parameter:**

**Question:** Is there a subfamily of at most sets,
such that

• The subfamily covers the universe

• SET COVER parameterized by the universe size is FPT – Algorithm with running time

– Based on dynamic programming

•

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### Dynamic programming for SET COVER

• Let

• We define a DP table for and

= min nr. of sets from needed to cover Or if impossible

• The value gives the minimum size of a set cover

– To solve the problem, compute using base cases and a recurrence

•

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### Filling the dynamic programming table

• = min nr. of sets from needed to cover

**Base case: **

if , otherwise it is

**Recursive step: **

• Skip set , or pay for and afterwards cover

• Each entry can be computed in polynomial time – entries in total

•

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### More on dynamic programming

• Dynamic programming is a memory-intensive algorithmic paradigm that yields FPT algorithms in various situations

– Here: dynamic programming over subsets of

– Later: dynamic programming over tree decompositions

• Research challenge:

– Determine whether the factor can be improved to for some

•

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