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What should be taught in approximation algorithms courses? Guy Kortsarz, Rutgers Camden. Advanced issues presented in many lecture notes and books:. Coloring a 3- colorable graph using vectors. Paper by Karger , Motwani and Sudan . Things a student needs to know:

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Advanced issues presented in many lecture notes and books
Advanced issues presented in many lecture notes and books:

  • Coloring a 3-colorable graph using vectors.

  • Paper by Karger, Motwani and Sudan.

  • Things a student needs to know:

    Separation oracle for: A is PSD.

    Getting a random vector inRn.

    This is done by choosing the Normal

    distributionat every entry.

    Given unit vector v, v . ris normal

    distribution.


Things a student needs to know
Things a student needs to know:

  • There is a choice of vectors vi for every i V so thatso that for every(i, j) E, vi · vj -1/2.


A student needs to know
A student needs to know:

  • S={i | r · vi }, threshold method, by now standard.

  • Sum of two normal distributions also normal.

  • Two inequalities (non trivial) about the normal distribution.

  • The above can be used to find a large independent set.

  • Combined with the greedy algorithm gives about n1/4 ratio approximation algorithm.


Advanced methods are also required in the following topics often taught
Advanced methods are also required in the following topics often taught:

  • The seminal result of Jain. With the simplification of Nagarajan et. al. 2-ratio for Steiner Network.

  • The beautiful 3/2 ratio by Calinesco, Karloff and Rabani, for Multiway Cuts: geometric

    embeddings.

  • Facharoenphol, Rao and Talwar, optimal random tree embedding. With this can get

    O(log n)for undirected multicut.


How to teach sparsest cut
How to teach sparsest cut? often taught:

  • Many still teach the embedding of a metric into L1, withO(log n)distortion. By Lineal, London, Rabinovich.

  • Advantage: relatively simple.

  • The huge challenge posed by the Arora, Rao and Vazirani result. Unweighted sparsest cutsqrt{log n}

  • Teach the difficult lemma? Very advance. Very difficult.

  • A proof appears in the book of Shmoys and Williamson.


Simpler topics
Simpler topics? often taught:

  • I can not complain if it is TAUGHT! Of course not. Let me give a list of basic topics that always taught

  • Ratio3/2 for TSP, the simple approximation of 2 for min cost Steiner tree.

  • Set-Cover , simple approximation ratio.

  • Knapsack,PTAS. Bin packing, constant ratio.

  • Set-Coverage. BUT:only costs 1.


Knapsack set coverage
Knapsack Set-Coverage often taught:

  • The Set-Coverage problem is given a set system and a numberkselectk sets that cover as many elemnts as possible.

  • Knapsack version, not that known:

  • Each set has cost c(s) and there is a bound B on the maximum sum of costs, of sets we can choose.

  • Maximize number of elements covered.


Result due to khuller moss and naor 1997 ipl
Result due to often taught:Khuller , Moss and,  Naor, 1997, IPL

  • The (1-1/e) ratio is possible.

  • In the usual algorithm & analysis (1-1/e) only follows if we can add the last set in the greedy choice. Thus, fails.

  • Because most times, adding the last set will give cost larger than B.

  • Trick: guess the 3 sets in OPTof least cost. Then apply greedy (don’t go over budget B).


Why do i know this paper
Why do I know this paper? often taught:

  • I became aware of this result only several years after published. And only because I worked on Min Power Problems. No conference version!

  • This result seems absolutely basic to me. Why is it no taught?

  • Remark: Choosing one (least cost) element of OPT gives unbounded ratio. Choosing two sets of smallest cost gives ratio ½. Guessing the three sets of least cost and then greedy gives(1-1/e).


First general neglected topic
First often taught:general neglected topic

  • Important and not taught: Maximizing a submodular non-decreasing function under Matroid Constrains, ratio 1/2, Fischer, Nemhauser, Wolsey, 1977.

  • Improved in 2008(!) to best possible (1-1/e) by Vondrak in a brilliant paper.


First story a submission i refereed
First story: a submission I refereed often taught:

  • I got a paper to referee, and it was obvious that it is maximize Submodular function under Matroid constrains

  • If memory serves, the capacity 1, of the following Matroid: G(V,E), edge capacities, fix S  V. T reaches S if every vertex in Tcan send one unit of flow toS.

  • The set of all T that reach S a special Matroid called Gammoid. Everything in this paper, known!

  • Asked Chekuri (everybody must have an oracle) what is the Matroid, and Chekuri answered. Paper erased.


Story 2 a worse outcome
Story 2: a worse outcome. often taught:

  • Problem. Input like Set-Cover but S= Si.

  • Required: choose at most one set of everySi and maximize the number of elements covered.

  • Paper gave ratio ½. This is maximizing submodular cover subject to partition Matroid. PLEASE!!! Do not try to check who the authors are. Not ethical. Unfair to authors, as well.

  • Nice applications, but was accepted and ratio not new.


Related to pipage rounding
Related to pipage rounding often taught:

  • Due to Ageev, Sviridenko.

  • Dependent rounding, is a generalization of

    Pipage rounding by Gandhi, Khuller, Parthasarathy, Srinivasan.

  • Say that we have an LP and a constraint

    xi=k. RR can not derive exact equality.

  • Pipage Rounding : instead of going to a larger set of solutions like IP to LP, we replace the objective function.


The principals of pipage rounding
The principals of pipage rounding often taught:

  • We start with LP maximization with function L(X).

  • Define a non linear function F.

  • Show that the maximum of F is integral.

  • Show that integral points of F belong to the Polyhedra of L. Namely feasible for L as long as it is integral, and feasible for F.


The principals of pipage rounding1
The principals of pipage rounding often taught:

  • Then, show that F(Xint ) ≥L(X* )/, for

    > 1.

  • HereXint is the (integral) optimumof F and

    X*the optimum fractional solution for L

  • Because Xint is known to be feasible for L(x) due to its integrality, it is feasible for L and thus  approximation.


Example max coverage
Example: Max Coverage often taught:

  • Max j wi zi

    S.T

     element j belongs to set i xi≥zj

     set i xi=p

    xi and zj are integral

    In Set Coverage we bound the number of sets.


The function f
The function often taught:F

  • F(x)=j wi (1-element j belongs to set i(1-xi) )

  • Definea function on a cycle.

  • As a function of .

  • The idea is to make plus  and then minus  over the cycle.

  • Make one entryonthe cycle smaller by  and another larger by .


The function f1
The function often taught:F

  • F(x)=j wi (1-element j belongs to set i(1-xj) )

  • The idea is to make plus  and then minus  all over the cycle.

  • But to show convexity we make just one entryonthe cycle smaller by  and another larger by .

  • The  appears as 2 in this term.


The function f2
The function often taught:F

  • As  appears as 2 in this term, the second derivative is positive.

  • Thus F is convex.

  • Which means that the maximum is in the borders.

  • For example for x2 between -4 and 3.

  • The maximum is in the border -4.


Changing the two by two
Changing the often taught: two by two

  • Putting plus and minus  alternating along a cycle make at least one entry integral.

  • Moreover, we can decompose a cycle into two matching and there are two ways to increase and decrease by .

  • One direction of the two makes the function not smaller.

  • This implies that the optimum of F is integral.


Thus the optimum of f is integral
Thus the optimum of often taught:F is integral

  • Its not hard to see that on integral vectors F and L have the same value.

  • Another inequality that is quite hard to prove is that: 1-i=1 to k (1-xi)≥(1-(1-1/k)k)L(X)

  • This gives a slightly better than 1-1/e ratio if k is small.


Submodularity related to very basic technique
Submodularity: related to very basic technique. often taught:

  • f is submodular if f(A)+f(B)f(AB)+f(AB)

  • Makes a lot of difference if non-decreasing or not. If not, in my opinion represent concave.

  • If non-decreasing, brings us to the next lost simple subject: Submodular cover problems.

  • Input: U and submodular non-decreasing function f and cost c(u) per item u.

  • Required: a set S of minimum cost so that f(S)=f(U).


Wolsey 1982 did much better
Wolsey often taught: , 1982, did much better

  • Each iteration pick item u so that helpu(S)/c(u) is maximum.

  • The ratio is max{uU}ln f(u)+1.

  • Example: For Set-Coverln|s|+1,s largest set.

  • Example: Same for Set-Cover with hard capacities. A paper in 1991, and one in 2002, did this result again (second was 20 years after Wolsey). Special case after 20 years! But its worse, yet.

  • Wolsey did better than that. Natural LP unbounded ratio even for Set-Cover with hard capacities.

  • Wolsey found a fabulous LP of gap max{uU}ln f(u)+1.


More general density
More general: often taught:density

  • Not taught at all but just cited. Why?

  • Here is a formal way:

  • Universe U and a function f: 2UR+

  • Each element in U has a cost c(u).

  • The function f not decreasing.

  • We want to find a minimum cost W so that f(W)=f(U).

  • We usually say, S U, c(S)=uS c(u)

  • But it works for an subadditive cost function


The density claim
The often taught:density claim

  • Say that we already created a set S via a greedy algorithm.

  • Now say that at any iteration we are able to find some Z so that:

    (f(Z+S)-f(S))/c(Z)≥(f(U)-f(S))/(δ·opt)

  • Then the final set S has cost bounded by

    (δ ln(U)+1) opt


What does it mean
What does it mean? often taught:

  • Think for the moment of δ=1.

  • Say that the current set S has no intersection with the optimum.

  • Then if we add all of OPT to S we certainly get a feasible solution.

  • Then clearlyf(S+OPT)=f(U)

  • And

  • (f(S+OPT)-f(S))/c(Z)≥ (f(OPT)-f(S))/c(OPT)

  • =(f(U)-f(S))/f(OPT)

  • It means that we found a solution to add that has the samedensity as adding OPT.


Proof continued
Proof continued often taught:

  • f(U) - j≤ i-1 f(Sj)≥ 1.

  • We may assume that the cost of every set added is at most

    opt, therefore c(Sj ) ≤opt

  • Therefore it remains to bound:

     j≤ i-1 c(Zi)

    Let us concentrate on what happens before

    Si is added.


By the previous claims
By the previous claims often taught:

  • 1 ≤ f(U)-f(Z1+Z2 +……Zi-1)≤

    Πj≤ i-1(1-c(Zi)/δ·opt)· f(U)

  • 1/f(U) ≤ Πj≤ i-1(1-c(Zi)/δ·opt)·

  • Take ln and use ln(1+x) ≤ x:

    -ln( f(U))≤ i≤ j -c(Zi) )/δ·opt

     i≤ j c(Zi) ≤opt δ ln( f(U))

    and so the ratio of (δ ln( f(U))+1) follows.


A paper of mine
A paper of mine often taught:

  • Min c x subject to ABx b, with A positive entries and B flow matrix. Ratio logarithmic.

  • We got much more general results. The above I was sure then and sure now, KNOWN and presented as known.

  • Referees: Cite, or prove submodularity! We had to prove (referees did not agree its known!).

  • Example: gives log n for directed Source Location. Maybe first time stated but I considered it known.

  • This log n was proved at least 4 times since then.


Remarks
Remarks often taught:

  • The bad thing about these 4 papers is not that did not know our paper (to be expected) but that they would think such a simple result is NOTKNOWN.

  • It is good to know the result of Wolsey: for example, used it recently (Hajiaghayi ,Khandekar,K , Nutov) to give a lower bound of about log 2 n for a problem in fashion: Capacitated NetworkDesign (Steiner network with capacities). First lower bound for hard capacities.


Dual fitting and a mistake we all make
Dual fitting and a mistake we all make often taught:

  • 1992. GK to Noga Alon:

  • This (spanner) result bares similarities to the proof done by Lovats for set-cover.

  • Noga Alon (seems very unhappy, maybe angry): Give me a break! That is folklore. Lovats told me he wrote it so he would have something to cite.......

  • Everybody cites Lovats here. Its simply not true.

  • We don’t know the basics. Result known many years before 1975.

  • Should we cite folklore? Yes!


How to teach dual fitting for set cover unweighted
HOW often taught: to teach dual fitting for set cover, unweighted?

  • Let S be the collection of sets and Tthe elements.

  • The dual, costs 1: MaximizetT yt

  • Subject to: ts yt  c(s)=1

  • We define a dual: if the greedy chose a star of length i, each element in the set gets 1/i

.2

.2

.2

.2

.2


The bound on the sum of elements of a given set
The bound on the sum of elements of a given set often taught:

1/2

1/2

1

1/7

1/6

1/7

1/5

1/7

1/4

1/4

1/3

1/12

1/12

1/11

1/12

1/10

1/9

1/12

1/8

1/12


Primal dual of gw
Primal Dual of GW often taught:

  • Goemans and Williamson gave a rather well known Primal-Dual algorithm. Always taught, and should be.

  • A question I asked quite several researchers and I don’t remember a correct response: Why reverse delete?

  • Why not Michael Jackson?

  • GW primal dual imitates recursion.

  • In LR reverse delete follows from recusrsion.


Local ratio for covering problems
Local Ratio for covering problems often taught:

  • Give weights to items so that every minimal. solution is a  approximation. Reduce items costs by weights chosen.

  • Elements of cost 0 enter the solution.

  • Make minimal.

  • Recurse.

  • No need for reverse delete. Recursion implies it.

  • Simpler for Steiner Network in my opinion.


Local ratio
Local Ratio often taught:

  • Without it I don’t think we could find a ratio 2 for Vertex feedback set.

  • A recent result of K, Langberg, Nutov. Minor result (main results are different) but solves an open problem of a very smart person: Krivelevich.

  • Covering triangles, gap2for LP (polynomial).

  • Open problem: tight?

  • Not only we showed tight family but showed as hard as approximatingVC.Used LR in proof.


Group steiner problem on trees
Group Steiner problem on trees often taught:

  • Group Steiner problem on trees.

  • Input : An undirected weighted rooted by r tree

    T = (V; E) and subsets S1,……,Sp V.

  • Goal: Find a tree in G that connects at least one vertex from each Si to r.

  • The Garg,Konjevod and Ravi proof while quite simple can be much much further simplified. In both proofs:

    O(log n· log p) ratio.

  • The easier (unpublished) proof is by Khandekar and Garg.


The theorem of garg konjevod and ravi
The theorem of Garg Konjevod and Ravi often taught:

  • There is an O(h log p)-approximation algorithm for Group Steiner on trees.

    T= (V; E) rooted at r has depth h.

  • Simple observation: we may assume that the groups only contain leaves by adding zero cost edges.

  • The GKR result uses an LP methods.


The fractional lp
The fractional LP often taught:

  • Minimize e cost(e)· xe

    frg=1 For every g.

    feg≤ xe

    fvg ≤ v’ child of v fvv’(g)

    fvg = fpar(v) v(g)

    The xeare capacities. Under that, the sum of flows from r to the leaves that belong to g is 1. If we set xe=1 for the edges of the optimum we get an optimum solution.

    Thus the above (fractional) LP is a relaxation.


The rounding method of gkr
The rounding method of often taught:GKR

  • Consider xe and say that its parent edge is (par(v),v)

  • Independently for every e, add it to the solution with probability xe/xpar(v)v

  • We show that the expected cost is bounded by the LP cost.

  • The probability that an edge gets to the root

    is a telescopic multiplication.


The probability that an edge is chosen
The probability that an edge is chosen often taught:

  • All terms cancel but the first and the last. The First is xe. The last is the flow from ‘ The parent of r to r ’ which we may assume is 1.

  • Since this is the case, xe contributes

    xe· cost(e) to the expected cost.

  • Therefore, the expected cost is the LP value which is at most the integral optimum. However: what is the probability that a group is covered?


The probability a group is covered
The probability a group is covered often taught:

  • Let v be a vertex at level I in the tree, then the probability that after rounding there is a path from v to a vertex in g is at least:

    fvg /((h-i+1)· xpar(v)v)


Let p v be this probability that the group is not covered
Let often taught:P(v) be this probability that the group is not covered

  • Let P(v) be the probability that there is no path from v to a leaf in group g. In the next inequalities a vertex v’ is always a child of v and the corresponding edge is e=(v,v’).

  • P(v)=Πv’ (1-(xe· (1-p(v’))/xpar(v)v )

  • Explanation: The probability for a group to get connected tov’ for some child v’ of v is (1-P(v’)). Given that, the probability that the edge (v,v’) gets selected is xe·/xpar(v)v . The multiplication is because the events are independent for different children.


Proof continued1
Proof continued often taught:

  • (1-P(v’)) is the probability that v’ can reach a leaf of g by a path after the randomized process.

  • By the induction assumption:

    (1-P(v’)) ≥fgv’ /((h-i+1)· xpar(v)v)

    Therefore:

    P(v)≤Π(1-xe·fgv’ /(xpar(v)v(h-i)·xe)=

    Π (1-fgv’ /(xpar(v)v(h-i))


Proof continued2
Proof continued often taught:

  • We use the inequality 1-x≤exp(-x) to get the inequality:

    P(v) ≤ exp(- fgv’ /(xpar(v)v(h-i))

  • From the constrains of the LP we get:

  • P(v) ≤exp( -fgv/(xpar(v)v(h-i)))


Ending the proof
Ending the proof often taught:

  • Use the inequality exp(1/(1-x))≤1-1/x to get:

  • P(v)≤ 1- fvg/((h-i-1) · xpar(v)v)

  • This ends the proof.

  • We now only have to consider v=r


Proof continued3
Proof continued often taught:

  • For the root we may think ofxpar(r)r=1

  • For the root frg=1 and thus the probability that a group is covered is at least 1/(h+1). The probability that a group is not covered in (h+1)· ln p iterations is at most

  • (1-1/(h+1))(h+1)·ln p exp(-ln p)=1/p


End of proof
End of proof. often taught:

  • Since a group is not covered with probability 1/p we can take every uncovered group and join it by a shortest path to r. A shortest path from any group member to r is at most opt.

  • Thus the expected cost of this final stage is:

    1/p· p · opt=opt

  • Thus the expected cost is (h+1)ln p· opt+opt


Making the h log n
Making the often taught:h=log n

  • Question: If the input for Group Steiner is a very tall tree to begin with. How do we get O(log2 n) ratio?

  • Use FRT? Looses a log n and complicated.

  • Basic but probably not widely known: Chekuri Even and Kortsarz show how to reduce the height of any tree to log n with a penalty 8 on the cost. Combinatorial!

  • In summary, we get an elementary analysis of

    O(log n· log p) approximation ratio for the Group

    Steiner on trees.


Recursive greedy
Recursive greedy often taught:

  • Never taught. Directed Steiner, basic problem.

  • A gem by Charikar et al. Say that the number of terminals to be covered is z. There is a child u inT* whose density is at most opt/z.

  • Let z’be the number of terminals in T*u

  • The analysis stops once we cover at leastz’/(h-1)terminals.Details omitted but gives telescopic multiplication that means density returned at most hopt/z.

  • Can make h O(1/) with ratio penalty n1/ (Zelikovsky). Time: larger but in the ball park of nh = nO(1/).


Alternative approximation algorithm for directed steiner
Alternative approximation algorithm for Directed Steiner often taught:

  • This was known (Chekuri told me) apparently in more complex form, since 1999.

  • The simpler way (as far as I know) Mendel and Nutov.

  • Create a graph H in which each path from the root r to some terminal u of length at most 1/,is a node.

  • There is a directed edge between p’ andp if pextendsp’ by one edge.

  • By the theorem of Zelikovsky, a solution of cost at mostO(n 1/ )opt isembedded inH.


A non recursive greedy approximation for directed steiner
A often taught:non recursive greedy approximation for Directed Steiner

  • For every terminal t, make a group Ht of all paths of length at most 1/ that start at r and end at t.

  • This reduces the problem to Group Steineron trees:Connect at least one terminal of Htby a path fromr , for every t . Our analysis works and it’s a page and a half.

  • This gives a non Recursive Greedy algorithm of two pages for Directed Steiner with same ratio: n. Only black box is the (very complex) height reduction CEK and the Zelikovsky theorem.


Certificate of failure
Certificate of failure often taught:

  • Many papers say that: 'The value opt of OPT is KNOWN'.

  • Knowing opt?? How can we know opt? Absurd. Means P=NP.

  • I first saw this in a paper of Hochbaum and Shmoys from J.ACM 1984. The paper is called: Powers of graphs: A powerful approximation technique for bottleneck problems.

  • Certificate of failure. Take. If  <opt the algorithm may return a set of size  opt.

  • Alternatively, may return failure. In this case  <opt. and then this hold true (this is why its certificate).

  • If mu\geq opt returns alpha\cdot opt cost solution.

  • Binary search. Get to mu and mu/2 with failure.

  • For mu, alpha\cdot opt cost solution.


Certificate of failure1
Certificate of failure often taught:

  • In case  >opt algorithm returnsa solution of cost at most  opt.

  • Binary search: fails for /2but succeeds with  . As /2<opt,and return a solution of cost at most  opt, the ratio is 2

  • Referees of my papers failed to understand that, many many times. Convention does not seem to be known to all. Should be!


Density lp useful and basic
Density LP: useful and basic often taught:

  • Say that you have an LP for a covering problem that has some good ratio.

  • But now you only want to cover k of the elements. For every element x, there will be a variable yx that says how much x is taken.

  • We write yx=k but then divide the sum by k which means that the objective value is also divided by k. Thus we try to solve a densityLP.


Density lp
Density LP often taught:

  • You can get the original ratio with penalty in the ratio of O(log 2 n)

  • Number of items inside the solution may be much more than k therefore if we can get exactly k may depend on the possibility of density decomposition.

  • I first was shown this (by Chekuri) about 6 years ago. What do I not know about LP now? I fear that a lot.


Application of the basics example 1
Application of the basics, example 1 often taught:

  • Broadcast problem, directed graph, Steiner set S.

  • A vertex r knows a message and the goal is to transmit it to all of S. Let K be the set that know the message and N those who don’t. At every round a directed matching from K to N.

  • The endpoint in N of the matching join K.

  • Minimize number of rounds.

  • Let k=|S|. Remark: Result obtained with Elkin.


Algorithm
Algorithm often taught:

  • Find u that has at least sqrt{k} terminals at distance at most opt from u.

  • Remove Tu with exactly sqrt{k} terminals from G and height at mostopt.Let N remaining vertices.

  • Iterate untill no such u.

  • Let K’ be the union of trees, R be the roots. Clearly number of roots at most sqrt{k}.

  • Can not employ recursion but can inform allK’ in 2sqrt{k}+2opt rounds.


To finish enough to inform distance opt dominating set d n
To finish enough to inform distance often taught:opt dominating set DN

  • Cover NS by trees rooted at D.No vertex in those trees has more than sqrt{k} terminals at distance opt. So informing the rest of N given D K,requires opt+sqrt {k} rounds.

  • How do we inform a distance opt dominating set?

  • Reduce to the minimization version of maximazing a non-decreasing submodular function under partition Matroid.


Define a new graph
Define a new graph often taught:

(k,n)

(k,n1)

(k,n2)

(k,np)

z

p

q

p

z

q

opt

opt

opt

n


Finding k s arborescence from r with minimum maximum outdegree
Finding often taught:k<|S|Arborescence from r with minimum maximum outdegree

s’

sqrt{k}

sqrt{k}

t

W

k

t’

sqrt{k}

1

1


Solution
Solution often taught:

  • Solution obtained with Khandekar and Nutov.

  • Edges that carry flow and an arborescence from r to W. Flow(W) non-decreasing submodular

  • We prove there exists a size sqrt{k/} feasible W. Non-trivial proof, omitted.

  • The capacity of vertices and edges, divided by is alsosqrt{k/}.

  • By the Wolsey theorem about sqrt{k/} ratio approximation.The LP gap is sqrt{k}!


Summary
Summary often taught:

  • It goes without saying that my opinions bound me only.

  • My intention is not to change courses for real. Will be presumptuous.

  • Will I follow my own advice? Yes.

  • Can not only use the wonderful existing slides.

  • The little man always had to struggle in very difficult circumstances.

  • Thank you


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