More Like this

1. Baby PIH: Parameterized Inapproximability of Min CSP

   https://doi.org/10.4230/LIPIcs.CCC.2024.27  

   Guruswami, Venkatesan; Ren, Xuandi; Sandeep, Sai (January 2024, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Santhanam, Rahul (Ed.)

   The Parameterized Inapproximability Hypothesis (PIH) is the analog of the PCP theorem in the world of parameterized complexity. It asserts that no FPT algorithm can distinguish a satisfiable 2CSP instance from one which is only (1-ε)-satisfiable (where the parameter is the number of variables) for some constant 0 < ε < 1. We consider a minimization version of CSPs (Min-CSP), where one may assign r values to each variable, and the goal is to ensure that every constraint is satisfied by some choice among the r × r pairs of values assigned to its variables (call such a CSP instance r-list-satisfiable). We prove the following strong parameterized inapproximability for Min CSP: For every r ≥ 1, it is W[1]-hard to tell if a 2CSP instance is satisfiable or is not even r-list-satisfiable. We refer to this statement as "Baby PIH", following the recently proved Baby PCP Theorem (Barto and Kozik, 2021). Our proof adapts the combinatorial arguments underlying the Baby PCP theorem, overcoming some basic obstacles that arise in the parameterized setting. Furthermore, our reduction runs in time polynomially bounded in both the number of variables and the alphabet size, and thus implies the Baby PCP theorem as well. 

   more » « less
2. Linear space streaming lower bounds for approximating CSPs

   https://doi.org/10.1145/3519935.3519983  

   Chou, Chi-Ning; Golovnev, Alexander; Sudan, Madhu; Velingker, Ameya; Velusamy, Santhoshini (June 2022, {STOC} '22: 54th Annual {ACM} {SIGACT} Symposium on Theory of Computing)

   Leonardi, Stefano; Gupta, Anupam (Ed.)

   We consider the approximability of constraint satisfaction problems in the streaming setting. For every constraint satisfaction problem (CSP) on n variables taking values in {0,…,q−1}, we prove that improving over the trivial approximability by a factor of q requires Ω(n) space even on instances with O(n) constraints. We also identify a broad subclass of problems for which any improvement over the trivial approximability requires Ω(n) space. The key technical core is an optimal, q−(k−1)-inapproximability for the Max k-LIN-mod q problem, which is the Max CSP problem where every constraint is given by a system of k−1 linear equations mod q over k variables. Our work builds on and extends the breakthrough work of Kapralov and Krachun (Proc. STOC 2019) who showed a linear lower bound on any non-trivial approximation of the MaxCut problem in graphs. MaxCut corresponds roughly to the case of Max k-LIN-mod q with k=q=2. For general CSPs in the streaming setting, prior results only yielded Ω(√n) space bounds. In particular no linear space lower bound was known for an approximation factor less than 1/2 for any CSP. Extending the work of Kapralov and Krachun to Max k-LIN-mod q to k>2 and q>2 (while getting optimal hardness results) is the main technical contribution of this work. Each one of these extensions provides non-trivial technical challenges that we overcome in this work. 

   more » « less
3. On Equivalence of Parameterized Inapproximability of k-Median, k-Max-Coverage, and 2-CSP

   https://doi.org/10.4230/LIPIcs.IPEC.2024.6  

   Karthik, C_S; Lee, Euiwoong; Manurangsi, Pasin (January 2024, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Bonnet, Édouard; Rzążewski, Paweł (Ed.)

   Parameterized Inapproximability Hypothesis (PIH) is a central question in the field of parameterized complexity. PIH asserts that given as input a 2-CSP on k variables and alphabet size n, it is 𝖶[1]-hard parameterized by k to distinguish if the input is perfectly satisfiable or if every assignment to the input violates 1% of the constraints. An important implication of PIH is that it yields the tight parameterized inapproximability of the k-maxcoverage problem. In the k-maxcoverage problem, we are given as input a set system, a threshold τ > 0, and a parameter k and the goal is to determine if there exist k sets in the input whose union is at least τ fraction of the entire universe. PIH is known to imply that it is 𝖶[1]-hard parameterized by k to distinguish if there are k input sets whose union is at least τ fraction of the universe or if the union of every k input sets is not much larger than τ⋅ (1-1/e) fraction of the universe. In this work we present a gap preserving FPT reduction (in the reverse direction) from the k-maxcoverage problem to the aforementioned 2-CSP problem, thus showing that the assertion that approximating the k-maxcoverage problem to some constant factor is 𝖶[1]-hard implies PIH. In addition, we present a gap preserving FPT reduction from the k-median problem (in general metrics) to the k-maxcoverage problem, further highlighting the power of gap preserving FPT reductions over classical gap preserving polynomial time reductions. 

   more » « less
4. Triplet Reconstruction and all other Phylogenetic CSPs are Approximation Resistant

   https://doi.org/10.1109/FOCS57990.2023.00024  

   Chatziafratis, Vaggos; Makarychev, Konstantin (November 2023, IEEE)

   We study the natural problem of Triplet Reconstruction (also known as Rooted Triplets Consistency or Triplet Clustering), originally motivated by applications in computational biology and relational databases (Aho, Sagiv, Szymanski, and Ullman, 1981) [2]: given n datapoints, we want to embed them onto the n leaves of a rooted binary tree (also known as a hierarchical clustering, or ultrametric embedding) such that a given set of m triplet constraints is satisfied. A triplet constraint i j · k for points i, j, k indicates that 'i, j are more closely related to each other than to k,' (in terms of distances d(i, j) ≤ d(i, k) and d(i, j) ≤ d(j, k)) and we say that a tree satisfies the triplet i j · k if the distance in the tree between i, j is smaller than the distance between i, k (or j, k). Among all possible trees with n leaves, can we efficiently find one that satisfies a large fraction of the m given triplets? Aho et al. (1981) [2] studied the decision version and gave an elegant polynomial-time algorithm that determines whether or not there exists a tree that satisfies all of the m constraints. Moreover, it is straightforward to see that a random binary tree achieves a constant 13-approximation, since there are only 3 distinct triplets i j|k, i k| j, j k · i (each will be satisfied w.p. 13). Unfortunately, despite more than four decades of research by various communities, there is no better approximation algorithm for this basic Triplet Reconstruction problem.Our main theorem-which captures Triplet Reconstruction as a special case-is a general hardness of approximation result about Constraint Satisfaction Problems (CSPs) over infinite domains (CSPs where instead of boolean values {0,1} or a fixed-size domain, the variables can be mapped to any of the n leaves of a tree). Specifically, we prove that assuming the Unique Games Conjecture [57], Triplet Reconstruction and more generally, every Constraint Satisfaction Problem (CSP) over hierarchies is approximation resistant, i.e., there is no polynomial-time algorithm that does asymptotically better than a biased random assignment.Our result settles the approximability not only for Triplet Reconstruction, but for many interesting problems that have been studied by various scientific communities such as the popular Quartet Reconstruction and Subtree/Supertree Aggregation Problems. More broadly, our result significantly extends the list of approximation resistant predicates by pointing to a large new family of hard problems over hierarchies. Our main theorem is a generalization of Guruswami, Håstad, Manokaran, Raghavendra, and Charikar (2011) [36], who showed that ordering CSPs (CSPs over permutations of n elements, e.g., Max Acyclic Subgraph, Betweenness, Non-Betweenness) are approximation resistant. The main challenge in our analyses stems from the fact that trees have topology (in contrast to permutations and ordering CSPs) and it is the tree topology that determines whether a given constraint on the variables is satisfied or not. As a byproduct, we also present some of the first CSPs where their approximation resistance is proved against biased random assignments, instead of uniformly random assignments. 

   more » « less
5. Bilu-Linial Stability, Certified Algorithms and the Independent Set Problem

   Angelidakis, H; Awasthi, P.; Blum, A.; Chatziafratis, V.; Dan, C. (September 2019, 27th Annual European Symposium on Algorithms (ESA 2019))

   We study the classic Maximum Independent Set problem under the notion of stability introduced by Bilu and Linial (2010): a weighted instance of Independent Set is γ-stable if it has a unique optimal solution that remains the unique optimal solution under multiplicative perturbations of the weights by a factor of at most γ ≥ 1. The goal then is to efficiently recover this “pronounced” optimal solution exactly. In this work, we solve stable instances of Independent Set on several classes of graphs: we improve upon previous results by solving \tilde{O}(∆/sqrt(log ∆))-stable instances on graphs of maximum degree ∆, (k − 1)-stable instances on k-colorable graphs and (1 + ε)-stable instances on planar graphs (for any fixed ε > 0), using both combinatorial techniques as well as LPs and the Sherali-Adams hierarchy. For general graphs, we give an algorithm for (εn)-stable instances, for any fixed ε > 0, and lower bounds based on the planted clique conjecture. As a by-product of our techniques, we give algorithms as well as lower bounds for stable instances of Node Multiway Cut (a generalization of Edge Multiway Cut), by exploiting its connections to Vertex Cover. Furthermore, we prove a general structural result showing that the integrality gap of convex relaxations of several maximization problems reduces dramatically on stable instances. Moreover, we initiate the study of certified algorithms for Independent Set. The notion of a γ-certified algorithm was introduced very recently by Makarychev and Makarychev (2018) and it is a class of γ-approximation algorithms that satisfy one crucial property: the solution returned is optimal for a perturbation of the original instance, where perturbations are again multiplicative up to a factor of γ ≥ 1 (hence, such algorithms not only solve γ-stable instances optimally, but also have guarantees even on unstable instances). Here, we obtain ∆-certified algorithms for Independent Set on graphs of maximum degree ∆, and (1 + ε)-certified algorithms on planar graphs. Finally, we analyze the algorithm of Berman and Fürer (1994) and prove that it is a ((∆+1)/3 + ε)-certified algorithm for Independent Set on graphs of maximum degree ∆ where all weights are equal to 1. 

   more » « less