Parameterized Approximation Schemes Using Graph Widths. Michael Lampis Research Institute for Mathematical Sciences Kyoto University
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1 Parameterized Approximation Schemes Using Graph Widths Michael Lampis Research Institute for Mathematical Sciences Kyoto University July 11th, 2014
2 Visit Kyoto for ICALP 15! Parameterized Approximation Schemes 2 / 23
3 Overview Topic of this talk: Randomized Parameterized Approximation Algorithms Approximation: Ratio of (1 + ǫ) Parameterized: Parameter is tree/clique-width Randomized: Probabilistic rounding Message: A generic technique for dealing with problems which are: W-hard: need time n k to solve exactly APX-hard: cannot be (1+ǫ) approximated in poly time Result: A natural (logn/ǫ) O(k) algorithm with ratio (1+ǫ) Parameterized Approximation Schemes 3 / 23
4 Overview Topic of this talk: Randomized Parameterized Approximation Algorithms Approximation: Ratio of (1 + ǫ) Parameterized: Parameter is tree/clique-width Randomized: Probabilistic rounding Message: A generic technique for dealing with problems which are: W-hard: need time n k to solve exactly APX-hard: cannot be (1+ǫ) approximated in poly time Result: A natural (logn/ǫ) O(k) algorithm with ratio (1+ǫ) Parameterized Approximation Schemes 3 / 23
5 Two concrete problems Max Cut parameterized by clique-width Given: Graph G(V,E) (along with a clique-width expression) Wanted: A partition of V into L,R that maximizes edges cut. Parameter: The clique-width of G (k). Parameterized Approximation Schemes 4 / 23
6 Two concrete problems Max Cut parameterized by clique-width Given: Graph G(V,E) (along with a clique-width expression) Wanted: A partition of V into L,R that maximizes edges cut. Parameter: The clique-width of G (k). Easy n k DP algorithm, known to be essentially optimal [Fomin et al. SODA 10] Parameterized Approximation Schemes 4 / 23
7 Two concrete problems Max Cut parameterized by clique-width Given: Graph G(V,E) (along with a clique-width expression) Wanted: A partition of V into L,R that maximizes edges cut. Parameter: The clique-width of G (k). Easy n k DP algorithm, known to be essentially optimal [Fomin et al. SODA 10] Capacitated Dominating Set parameterized by treewidth Given: Graph G(V,E), capacity c : V N Wanted: Min size dominating set + domination plan... selected vertex u can dominate at most c(u) vertices Parameter: treewidth of G (k). Parameterized Approximation Schemes 4 / 23
8 Two concrete problems Max Cut parameterized by clique-width Given: Graph G(V,E) (along with a clique-width expression) Wanted: A partition of V into L,R that maximizes edges cut. Parameter: The clique-width of G (k). Easy n k DP algorithm, known to be essentially optimal [Fomin et al. SODA 10] Capacitated Dominating Set parameterized by treewidth Given: Graph G(V,E), capacity c : V N Wanted: Min size dominating set + domination plan... selected vertex u can dominate at most c(u) vertices Parameter: treewidth of G (k). Easy C k algorithm, C max capacity. Known to be W-hard [Dom et al. IWPEC 08] Parameterized Approximation Schemes 4 / 23
9 Treewidth - Pathwidth reminder Good tree/path decompositions give a sequence of small separators Parameterized Approximation Schemes 5 / 23
10 Treewidth - Pathwidth reminder Good tree/path decompositions give a sequence of small separators Parameterized Approximation Schemes 5 / 23
11 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. Parameterized Approximation Schemes 6 / 23
12 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. Parameterized Approximation Schemes 6 / 23
13 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. Parameterized Approximation Schemes 6 / 23
14 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. Parameterized Approximation Schemes 6 / 23
15 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. Parameterized Approximation Schemes 6 / 23
16 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. Parameterized Approximation Schemes 6 / 23
17 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. Parameterized Approximation Schemes 6 / 23
18 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. Parameterized Approximation Schemes 6 / 23
19 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. Parameterized Approximation Schemes 6 / 23
20 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. Parameterized Approximation Schemes 6 / 23
21 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. For Dominating Set only need to remember information about boundary Selected (Blue) Not Covered (Red) Not Selected Already Covered (Green) Total Cost Parameterized Approximation Schemes 6 / 23
22 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. For Dominating Set only need to remember information about boundary Selected (Blue) Not Covered (Red) Not Selected Already Covered (Green) Total Cost Separator: {3, 4, 5, 6} includes tuple (3,4,5,6;?) Parameterized Approximation Schemes 6 / 23
23 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. For Dominating Set only need to remember information about boundary Selected (Blue) Not Covered (Red) Not Selected Already Covered (Green) Total Cost Separator: {3, 4, 5, 6} includes tuple (3,4,5,6;2) Parameterized Approximation Schemes 6 / 23
24 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. For Dominating Set only need to remember information about boundary Selected (Blue) Not Covered (Red) Not Selected Already Covered (Green) Total Cost Separator: {3, 4, 5, 6} includes tuple (3,4,5,6;2) Parameterized Approximation Schemes 6 / 23
25 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. For Dominating Set only need to remember information about boundary Selected (Blue) Not Covered (Red) Not Selected Already Covered (Green) Total Cost Separator: {3, 4, 5, 6} includes tuple (3,4,5,6;2) Separator: {3, 4, 5, 7} includes tuple (3,4,5,7;2) Parameterized Approximation Schemes 6 / 23
26 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. For Dominating Set only need to remember information about boundary Selected (Blue) Not Covered (Red) Not Selected Already Covered (Green) Total Cost Separator: {3, 4, 5, 6} includes tuple (3,4,5,6;2) Separator: {3, 4, 5, 7} includes tuple (3,4,5,7;3) Parameterized Approximation Schemes 6 / 23
27 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. For Dominating Set only need to remember information about boundary Selected (Blue) Not Covered (Red) Not Selected Already Covered (Green) Total Cost Separator: {3, 4, 5, 6} includes tuple (3,4,5,6;2) Separator: {3, 4, 5, 7} includes tuple (3,4,5,7;3) Separator: {4, 5, 7, 8} includes tuple (4,5,7,8;3) Parameterized Approximation Schemes 6 / 23
28 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. For Dominating Set only need to remember information about boundary Selected (Blue) Not Covered (Red) Not Selected Already Covered (Green) Total Cost Separator: {3, 4, 5, 6} includes tuple (3,4,5,6;2) Separator: {3, 4, 5, 7} includes tuple (3,4,5,7;3) Separator: {4, 5, 7, 8} includes tuple (4,5,7,8;4) Parameterized Approximation Schemes 6 / 23
29 Algorithmic view The reason that this decomposition of the graph is useful is that we have a moving boundary of small separators that sweeps the graph. For Dominating Set only need to remember information about boundary Selected (Blue) Not Covered (Red) Not Selected Already Covered (Green) Total Cost For Dominating Set DP tables have size 3 k. For Capacitated Dominating Set must remember capacity info for selected vertices Table Size: C k Note: May remember Capacity left OR Capacity used. Same thing? Parameterized Approximation Schemes 6 / 23
30 Why n k for Max Cut? (1/2) A labelled graph G has clique-width at most k if G is K 1 with some label in {1,...,k} Union: G = G 1 G 2, with cw k Join: G = Join(i,j,G ), i,j {1,...,k} and G has cw k Rename: G = Rename(i j,g ), i,j {1,...,k} and G has cw k Parameterized Approximation Schemes 7 / 23
31 Why n k for Max Cut? (1/2) A labelled graph G has clique-width at most k if G is K 1 with some label in {1,...,k} Union: G = G 1 G 2, with cw k Join: G = Join(i,j,G ), i,j {1,...,k} and G has cw k Rename: G = Rename(i j,g ), i,j {1,...,k} and G has cw k Example: Join(1,2) Rename(3 2) Parameterized Approximation Schemes 7 / 23
32 Why n k for Max Cut? (1/2) A labelled graph G has clique-width at most k if G is K 1 with some label in {1,...,k} Union: G = G 1 G 2, with cw k Join: G = Join(i,j,G ), i,j {1,...,k} and G has cw k Rename: G = Rename(i j,g ), i,j {1,...,k} and G has cw k Example: Join(1,2) Rename(3 2) Parameterized Approximation Schemes 7 / 23
33 Why n k for Max Cut? (1/2) A labelled graph G has clique-width at most k if G is K 1 with some label in {1,...,k} Union: G = G 1 G 2, with cw k Join: G = Join(i,j,G ), i,j {1,...,k} and G has cw k Rename: G = Rename(i j,g ), i,j {1,...,k} and G has cw k Parameterized Approximation Schemes 7 / 23
34 Why n k for Max Cut? (1/2) A labelled graph G has clique-width at most k if G is K 1 with some label in {1,...,k} Union: G = G 1 G 2, with cw k Join: G = Join(i,j,G ), i,j {1,...,k} and G has cw k Rename: G = Rename(i j,g ), i,j {1,...,k} and G has cw k A clique-width expression for G is a proof that G can be built using these operations and k labels. Finding an optimal expression is generally hard... We hope that such an expression is supplied. We view it as a binary tree and perform dynamic programming. Parameterized Approximation Schemes 7 / 23
35 Why n k for Max Cut? (2/2) Natural dynamic program for Max Cut For each node store a collection of tuples (l 1,l 2,...,l k ;C) Meaning: There exists a solution that places exactly l i vertices with label i in L and cuts C edges. Parameterized Approximation Schemes 8 / 23
36 Why n k for Max Cut? (2/2) Natural dynamic program for Max Cut For each node store a collection of tuples (l 1,l 2,...,l k ;C) Meaning: There exists a solution that places exactly l i vertices with label i in L and cuts C edges. Example tuple: (red = L) (1,1,2;3) Parameterized Approximation Schemes 8 / 23
37 Why n k for Max Cut? (2/2) Natural dynamic program for Max Cut For each node store a collection of tuples (l 1,l 2,...,l k ;C) Meaning: There exists a solution that places exactly l i vertices with label i in L and cuts C edges. Example tuple: (red = L) (1,1,2;6) Parameterized Approximation Schemes 8 / 23
38 Why n k for Max Cut? (2/2) Natural dynamic program for Max Cut For each node store a collection of tuples (l 1,l 2,...,l k ;C) Meaning: There exists a solution that places exactly l i vertices with label i in L and cuts C edges. Example tuple: (red = L) (1,3,0;6) Parameterized Approximation Schemes 8 / 23
39 Why n k for Max Cut? (2/2) Natural dynamic program for Max Cut For each node store a collection of tuples (l 1,l 2,...,l k ;C) Meaning: There exists a solution that places exactly l i vertices with label i in L and cuts C edges. Can prove inductively that all entries corresponding to potential cuts are filled in. Algorithm must compute up to (n/k) k entries for each node of the clique-width expression. Parameterized Approximation Schemes 8 / 23
40 Why n k for Max Cut? (2/2) Natural dynamic program for Max Cut For each node store a collection of tuples (l 1,l 2,...,l k ;C) Meaning: There exists a solution that places exactly l i vertices with label i in L and cuts C edges. Can prove inductively that all entries corresponding to potential cuts are filled in. Algorithm must compute up to (n/k) k entries for each node of the clique-width expression. Today s idea: keep rounded values for the l i entries. This can make the table smaller. Parameterized Approximation Schemes 8 / 23
41 What is rounding? Example rounding scheme: Normal table has values l i {0,1,2,3,...,n}. We can store values l i {0,1,2,4,8,16,...,n}. Informal meaning: there exists a partition that places roughly l i vertices with label i in L Running time table size (logn) k But approximation ratio 2 Parameterized Approximation Schemes 9 / 23
42 What is rounding? Example rounding scheme: Normal table has values l i {0,1,2,3,...,n}. Fix some (small) parameter δ > 0 We will store values l i {0,(1+δ),(1+δ) 2,(1+δ) 3,...} Informal meaning: there exists a partition that places roughly l i vertices with label i in L Running time table size For small δ we have log (1+δ) n = O( logn ln(1+δ) ) = O(logn δ ) Table size (logn/δ) k Parameterized Approximation Schemes 9 / 23
43 What is rounding? Example rounding scheme: Normal table has values l i {0,1,2,3,...,n}. Fix some (small) parameter δ > 0 We will store values l i {0,(1+δ),(1+δ) 2,(1+δ) 3,...} Informal meaning: there exists a partition that places roughly l i vertices with label i in L Running time table size For small δ we have log (1+δ) n = O( logn ln(1+δ) ) = O(logn δ ) Table size (logn/δ) k Approximation ratio depends on choice of δ, but is at least (1+δ). This is achieved if we have the correct/best approximation for each value. Parameterized Approximation Schemes 9 / 23
44 What is rounding? Example rounding scheme: Normal table has values l i {0,1,2,3,...,n}. Fix some (small) parameter δ > 0 We will store values l i {0,(1+δ),(1+δ) 2,(1+δ) 3,...} Informal meaning: there exists a partition that places roughly l i vertices with label i in L Running time table size For small δ we have log (1+δ) n = O( logn ln(1+δ) ) = O(logn δ ) Table size (logn/δ) k Approximation ratio depends on choice of δ, but is at least (1+δ). This is achieved if we have the correct/best approximation for each value. This will be hard! Parameterized Approximation Schemes 9 / 23
45 The problem with rounding Errors can propagate and pile up! Parameterized Approximation Schemes 10 / 23
46 The problem with rounding Errors can propagate and pile up! Concrete example Example tuple: (red = L) (a 1,a 2,a 3 ;a C ) Parameterized Approximation Schemes 10 / 23
47 The problem with rounding Errors can propagate and pile up! Concrete example Example tuple: (red = L) (a 1,a 2 +a 3,0;a C ) Parameterized Approximation Schemes 10 / 23
48 The problem with rounding Errors can propagate and pile up! The new value we would like to store (a 2 +a 3 ) is not necessarily round (integer power of (1+δ)). We must somehow round it to fit the scheme This can introduce an additional error of (1+δ) Parameterized Approximation Schemes 10 / 23
49 The problem with rounding Errors can propagate and pile up! The new value we would like to store (a 2 +a 3 ) is not necessarily round (integer power of (1+δ)). We must somehow round it to fit the scheme This can introduce an additional error of (1+δ) After n steps this can cause an error of (1+δ) n Parameterized Approximation Schemes 10 / 23
50 The problem with rounding Errors can propagate and pile up! The new value we would like to store (a 2 +a 3 ) is not necessarily round (integer power of (1+δ)). We must somehow round it to fit the scheme This can introduce an additional error of (1+δ) After n steps this can cause an error of (1+δ) n Parameterized Approximation Schemes 10 / 23
51 The problem with rounding Errors can propagate and pile up! The new value we would like to store (a 2 +a 3 ) is not necessarily round (integer power of (1+δ)). We must somehow round it to fit the scheme This can introduce an additional error of (1+δ) After n steps this can cause an error of (1+δ) n Running time: (logn/δ) k. Want this to be (logn) O(k) so δ = 1/log c n. Then (1+δ) n is too big! (Certainly not 1+ǫ) Must round in a way that ensures sometimes rounding improves my approximation. Parameterized Approximation Schemes 10 / 23
52 The problem with rounding Errors can propagate and pile up! The new value we would like to store (a 2 +a 3 ) is not necessarily round (integer power of (1+δ)). We must somehow round it to fit the scheme This can introduce an additional error of (1+δ) After n steps this can cause an error of (1+δ) n Running time: (logn/δ) k. Want this to be (logn) O(k) so δ = 1/log c n. Then (1+δ) n is too big! (Certainly not 1+ǫ) Must round in a way that ensures sometimes rounding improves my approximation. Parameterized Approximation Schemes 10 / 23
53 How to measure errors Plan so far: Start with exact DP. Run it with approximate values. TBD: how to re-round non-round intermediate values. There is a value x calculated by the exact DP There is a value y calculated by approximate DP Define Error(x,y) := log (1+δ) (max{ x y, y x }) Parameterized Approximation Schemes 11 / 23
54 How to measure errors Plan so far: Start with exact DP. Run it with approximate values. TBD: how to re-round non-round intermediate values. There is a value x calculated by the exact DP There is a value y calculated by approximate DP Define In pictures: Error(x,y) := log (1+δ) (max{ x y, y x }) Parameterized Approximation Schemes 11 / 23
55 How to measure errors Plan so far: Start with exact DP. Run it with approximate values. TBD: how to re-round non-round intermediate values. There is a value x calculated by the exact DP There is a value y calculated by approximate DP Define In pictures: Error(x,y) := log (1+δ) (max{ x y, y x }) Parameterized Approximation Schemes 11 / 23
56 How to measure errors Plan so far: Start with exact DP. Run it with approximate values. TBD: how to re-round non-round intermediate values. There is a value x calculated by the exact DP There is a value y calculated by approximate DP Define In pictures: Error(x,y) := log (1+δ) (max{ x y, y x }) Parameterized Approximation Schemes 11 / 23
57 How to measure errors Plan so far: Start with exact DP. Run it with approximate values. TBD: how to re-round non-round intermediate values. There is a value x calculated by the exact DP There is a value y calculated by approximate DP Define In pictures: Error(x,y) := log (1+δ) (max{ x y, y x }) Parameterized Approximation Schemes 11 / 23
58 How to measure errors Plan so far: Start with exact DP. Run it with approximate values. TBD: how to re-round non-round intermediate values. There is a value x calculated by the exact DP There is a value y calculated by approximate DP Define End goal: Error(x,y) := log (1+δ) (max{ x y, y x }) Would like Error(x, y) ǫ/δ for all x, y. Approximation ratio = (1+δ) Error (1+δ) ǫ/δ 1+ǫ Parameterized Approximation Schemes 11 / 23
59 What we know about errors Consider values x 1,x 2 and their approximations y 1,y 2 with Errors E 1,E 2. Parameterized Approximation Schemes 12 / 23
60 What we know about errors Consider values x 1,x 2 and their approximations y 1,y 2 with Errors E 1,E 2. The (non-round) value y 1 +y 2 has error at most max{e 1,E 2 }. Parameterized Approximation Schemes 12 / 23
61 What we know about errors Consider values x 1,x 2 and their approximations y 1,y 2 with Errors E 1,E 2. The (non-round) value y 1 +y 2 has error at most max{e 1,E 2 }. The (non-round) value y 1 y 2 has error at most E 1 +E 2. Parameterized Approximation Schemes 12 / 23
62 What we know about errors Consider values x 1,x 2 and their approximations y 1,y 2 with Errors E 1,E 2. The (non-round) value y 1 +y 2 has error at most max{e 1,E 2 }. The (non-round) value y 1 y 2 has error at most E 1 +E 2. The (non-round) value y 1 y 2 has unbounded error! Parameterized Approximation Schemes 12 / 23
63 What we know about errors Consider values x 1,x 2 and their approximations y 1,y 2 with Errors E 1,E 2. The (non-round) value y 1 +y 2 has error at most max{e 1,E 2 }. The (non-round) value y 1 y 2 has error at most E 1 +E 2. The (non-round) value y 1 y 2 has unbounded error! DPs relying on additions are the Easiest Target. From now on only Additive DPs considered. Fortunately, there are plenty... E.g. Max Cut, Capacitated Dominating Set Parameterized Approximation Schemes 12 / 23
64 Two roads to success Obliviously round in some way. Hope for the best! Probabilistically round. Prove that good things happen whp. Parameterized Approximation Schemes 13 / 23
65 The lucky man s solution Consider a DP that only uses additions. Trivial observation: each level of the given clique-width expression/tree decomposition increases maximum Error by at most 1. Error can only be introduced in re-rounding. What if the given decomposition is balanced? Then it has logarithmic height! Wouldn t this be nice? Parameterized Approximation Schemes 14 / 23
66 The lucky man s solution Consider a DP that only uses additions. Trivial observation: each level of the given clique-width expression/tree decomposition increases maximum Error by at most 1. Error can only be introduced in re-rounding. What if the given decomposition is balanced? Then it has logarithmic height! Wouldn t this be nice? Parameterized Approximation Schemes 14 / 23
67 The lucky man s solution Consider a DP that only uses additions. Trivial observation: each level of the given clique-width expression/tree decomposition increases maximum Error by at most 1. Error can only be introduced in re-rounding. What if the given decomposition is balanced? Then it has logarithmic height! Wouldn t this be nice? Thm [Bodlaender and Hagerup SICOMP 98]: Every graph with treewidth w has a balanced tree decomposition with width 3w. Parameterized Approximation Schemes 14 / 23
68 Using our gift 1. Set δ = ǫ/logn. 2. Balance decomposition. 3. Run approximate DP, rounding arbitrarily. This works! (As long as we only do additions/comparisons) Approximation ratio (1+δ) logn (1+ǫ). Running time (logn/ǫ) O(k). Application approximation schemes: Capacitated Dom. Set (bi-criteria) Capacitated Vertex Cover (bi-criteria) Bounded Degree Deletion (bi-criteria) Equitable Coloring (bi-criteria) Graph Balancing Parameterized Approximation Schemes 15 / 23
69 Back to the Interesting Part
70 We have to round What about Max Cut on clique-width? Best known balancing theorem blows up number of labels to 2 k Must round in a way that works for n steps. Intuition: randomization evens out the errors. Process: We denote the (random) outcome of this process by y 1 y 2 Parameterized Approximation Schemes 17 / 23
71 We have to round What about Max Cut on clique-width? Best known balancing theorem blows up number of labels to 2 k Must round in a way that works for n steps. Intuition: randomization evens out the errors. Process: We denote the (random) outcome of this process by y 1 y 2 Parameterized Approximation Schemes 17 / 23
72 We have to round What about Max Cut on clique-width? Best known balancing theorem blows up number of labels to 2 k Must round in a way that works for n steps. Intuition: randomization evens out the errors. Process: We denote the (random) outcome of this process by y 1 y 2 Parameterized Approximation Schemes 17 / 23
73 Addition Trees We want this process to work whp for δ = Ω(1/poly(logn)). This is complicated. So we abstract it out. Definition: An Addition Tree (AT) is a binary tree with positive integers on the leaves. The value of each node is the sum of its children. Definition: An Approximate Addition Tree (AAT) is an Addition Tree where additions are replaced by the operation. Motivation: If AATs are good whp, we can use this as a black box for any DP that only does additions. Parameterized Approximation Schemes 18 / 23
74 Addition Trees We want this process to work whp for δ = Ω(1/poly(logn)). This is complicated. So we abstract it out. Definition: An Addition Tree (AT) is a binary tree with positive integers on the leaves. The value of each node is the sum of its children. Definition: An Approximate Addition Tree (AAT) is an Addition Tree where additions are replaced by the operation. Motivation: If AATs are good whp, we can use this as a black box for any DP that only does additions. Theorem: For any n-vertex AAT T and any ǫ > 0, there exists δ = Ω(ǫ 2 /log 6 n) such that: Pr[ v T : Error(v) > 1+ǫ] n logn Parameterized Approximation Schemes 18 / 23
75 Black Box Applications Application approximation schemes for clique-width: Max Cut Edge Dominating Set Is DP additive? Capacitated Dom. Set (bi-criteria) Bounded Degree Deletion (bi-criteria) Equitable Coloring (bi-criteria) Running times (logn/ǫ) O(k) Recall: last three are W-hard even for treewidth Parameterized Approximation Schemes 19 / 23
76 AAT theorem proof sketch Intuition for main Approximate Addition Tree theorem. Two main cases: Parameterized Approximation Schemes 20 / 23
77 AAT theorem proof sketch Intuition for main Approximate Addition Tree theorem. Two main cases: Balanced Tree: easy Parameterized Approximation Schemes 20 / 23
78 AAT theorem proof sketch Intuition for main Approximate Addition Tree theorem. Two main cases: UnBalanced Tree: not so easy Parameterized Approximation Schemes 20 / 23
79 AAT theorem proof sketch Intuition for main Approximate Addition Tree theorem. Proof Strategy: Prove the theorem for UnBalanced Trees Main part Define notion of balanced height Use induction Base case: UnBalanced trees Inductive step similar to UnBalanced case Parameterized Approximation Schemes 20 / 23
80 Unbalanced case Intuition: self-correcting random walk n addition + rounding, each can increase Error by 1. In the end we should have error at most log c n Parameterized Approximation Schemes 21 / 23
81 Unbalanced case Intuition: self-correcting random walk Observation 1: Each rounding step has in expectation no effect. Parameterized Approximation Schemes 21 / 23
82 Unbalanced case Intuition: self-correcting random walk Observation 1: Each rounding step has in expectation no effect. p is the probability of rounding down Parameterized Approximation Schemes 21 / 23
83 Unbalanced case Intuition: self-correcting random walk Observation 1: Each rounding step has in expectation no effect. 1 p is the probability of rounding up Parameterized Approximation Schemes 21 / 23
84 Unbalanced case Intuition: self-correcting random walk Observation 1: Each rounding step has in expectation no effect. If we round down we decrease our error by 1 p Parameterized Approximation Schemes 21 / 23
85 Unbalanced case Intuition: self-correcting random walk Observation 1: Each rounding step has in expectation no effect. If we round down we decrease our error by 1 p If we round up we increase our error by p Parameterized Approximation Schemes 21 / 23
86 Unbalanced case Intuition: self-correcting random walk Observation 1: Each rounding step has in expectation no effect. If we round down we decrease our error by 1 p If we round up we increase our error by p Expected change: p(1 p)+(1 p)p = 0 Parameterized Approximation Schemes 21 / 23
87 Unbalanced case Intuition: self-correcting random walk Observation 1: Each rounding step has in expectation no effect. Unfortunately, this observation is not enough! Parameterized Approximation Schemes 21 / 23
88 Unbalanced case Intuition: self-correcting random walk Observation 1: Each rounding step has in expectation no effect. Unfortunately, this observation is not enough! Token will end up at distance n whp. We need distance ǫ/δ log c n Parameterized Approximation Schemes 21 / 23
89 Unbalanced case Intuition: self-correcting random walk Observation 1: Each rounding step has in expectation no effect. Unfortunately, this observation is not enough! Observation 2: In UnBalanced tree, initial approximate value y 1 +y 2 always has improved error. Informally: one value is known without error y 1 = (1+δ) E 1 x 1 y 2 = (1+δ) 0 x 2 y 1 +y 2 = (1+δ) E 1 x 1 +x 2 < (1+δ) E 1 (x 1 +x 2 ) Parameterized Approximation Schemes 21 / 23
90 Unbalanced case Intuition: self-correcting random walk Observation 1: Each rounding step has in expectation no effect. Unfortunately, this observation is not enough! Observation 2: In UnBalanced tree, initial approximate value y 1 +y 2 always has improved error. Summary: Step 1: Obtain initial approximation improves Error Step 2: Round In expectation does not change Error stronger concentration than just random walk. This can be proved with moment-generating function (similar to Chernoff bound/azuma inequality etc.) Parameterized Approximation Schemes 21 / 23
91 Unbalanced case Intuition: self-correcting random walk Observation 1: Each rounding step has in expectation no effect. Unfortunately, this observation is not enough! Observation 2: In UnBalanced tree, initial approximate value y 1 +y 2 always has improved error. Summary: Step 1: Obtain initial approximation improves Error Step 2: Round In expectation does not change Error stronger concentration than just random walk. This can be proved with moment-generating function (similar to Chernoff bound/azuma inequality etc.) UnBalanced Trees are OK Parameterized Approximation Schemes 21 / 23
92 Summary Further Work Recap: (Randomized) Parameterized Approximation Algorithms for several problems. General Approximation Result for AATs. Further questions: Concrete: Hamiltonicity on clique-width General: Deal with other operations (subtraction?) Soft: Other applications of AATs? Problems W-hard on trees? (e.g. parameterized by degree) Parameterized Approximation Schemes 22 / 23
93 Thank you! Questions? Parameterized Approximation Schemes 23 / 23
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