Posts Tagged ‘이의웅’

Euiwoong Lee (이의웅), Faster Exact and Approximate Algorithms for k-Cut

Saturday, September 8th, 2018
Faster Exact and Approximate Algorithms for k-Cut
Euiwoong Lee (이의웅)
Courant Institute of Mathematical Sciences, NYU, New York, USA
2018/11/13 Tue 5PM (Bldg. E6-1, Room 1401)
In the k-cut problem, we are given an edge-weighted graph G and an integer k, and have to remove a set of edges with minimum total weight so that G has at least k connected components. This problem has been studied various algorithmic perspectives including randomized algorithms, fixed-parameter tractable algorithms, and approximation algorithms. Their proofs of performance guarantees often reveal elegant structures for cuts in graphs.
It has still remained an open problem to (a) improve the runtime of exact algorithms, and (b) to get better approximation algorithms. In this talk, I will give an overview on recent progresses on both exact and approximation algorithms. Our algorithms are inspired by structural similarities between k-cut and the k-clique problem.

Euiwoong Lee (이의웅), Bridging Continuous and Discrete Optimization through the Lens of Approximation

Saturday, September 8th, 2018

[FYI: Colloquium, School of Computing]

Bridging Continuous and Discrete Optimization through the Lens of Approximation
Euiwoong Lee (이의웅)
Courant Institute of Mathematical Sciences, NYU, New York, USA
2018/11/12 Mon 4PM (Bldg. E3-1, Room 1501)
Mathematical optimization is a field that studies algorithms, given an objective function and a feasible set, to find the element that maximizes or minimizes the objective function from the feasible set. While most interesting optimization problems are NP-hard and unlikely to admit efficient algorithms that find the exact optimal solution, many of them admit efficient “approximation algorithms” that find an approximate optimal solution.
Continuous and discrete optimization are the two main branches of mathematical optimization that have been primarily studied in different contexts. In this talk, I will introduce my recent results that bridge some of important continuous and discrete optimization problems, such as matrix low-rank approximation and Unique Games. At the heart of the connection is the problem of approximately computing the operator norm of a matrix with respect to various norms, which will allow us to borrow mathematical tools from functional analysis.

Euiwoong Lee (이의웅), FPT Approximation Algorithms for Graph Problems

Friday, June 9th, 2017
FPT Approximation Algorithms for Graph Problems
Euiwoong Lee (이의웅)
Computer Science Department, Carnegie Mellon University
2017/07/06 Thu 4PM-5PM
Approximation algorithms and fixed-parameter tractable (FPT) algorithms have been two major ways to deal with NP-hardness of combinatorial optimization problems. The notion of FPT approximation can be naturally defined, and it is getting significant attention recently. Starting from gentle introductions to approximation algorithms and FPT algorithms, I will talk about my three recent results on FPT approximability.
– Given a graph G = (V, E) and an integer k, we study k-Vertex Separator, where the goal is to remove the minimum number of vertices such that each connected component in the resulting graph has at most k vertices. We give an O(log k)-FPT approximation algorithm for k-Vertex Separator. Our result improves the best previous graph partitioning algorithms.
– We also study k-Path Transversal, where the goal is to remove the minimum number of vertices such that there is no simple path of length k. We present an O(log k)-FPT approximation algorithm for k-Path Transversal. There was no nontrivial approximation algorithm for k > 4 before this work.
– Finally, k-cut is the problem where we want to remove the minimum number of edges such that the graph has at least k connected components. We give a (2 – ε)-FPT approximation algorithm for some epsilon > 0, improving upon a (non-FPT) 2-approximation.
The third result is joint work with Anupam Gupta and Jason Li.

[FYI] KAIST Theory Day (May 31, 2015)

Monday, May 11th, 2015

FYI: (KAIST Theory Day organized by Jinwoo Shin and Eric Vigoda)

KAIST Theory Day
2015/5/31 Sun 10AM-5PM (Building N1, room 102, KAIST)
9:30 – Coffee and refreshments
10:00 – Daniel Stefankovic (Rochester):  Spin models: connections between complexity, phase transition, belief propagation, and induced matrix norms
11:00 – Yitong Yin (Nanjing)Phase transition of hypergraph matchings
12:00 – Lunch break
1:30 – Euiwoong Lee (CMU):  Hardness of Graph Pricing through Generalized Max-Dicut
2:30 – Sewoong Oh (UIUC):  Data processing inequality for differential privacy and applications
3:30 – Coffee break
4:00 – Martin Ziegler (TU Darmstadt):  Algebraic Complexity Theory and Quantum Logic

Speaker: Daniel Stefankovic

Title: Spin models: connections between complexity, phase transition, belief propagation, and induced matrix norms

What are the typical configurations of a spin model (for example, Ising model, or Potts model) on a random regular graph? We show that the answer to this question is characterized by tree recursions (belief propagation) and p->q operator matrix norms.

Understanding the typical configurations allows us to show hardness of approximating the partition function for certain multispin models (for example, Potts model) on d-regular graphs in the non-uniqueness regime (that is, when the Gibbs measure on infinite d-regular tree is not unique). Joint work with Andreas Galanis, and Eric Vigoda.

Speaker: Yitong Yin

Title: Phase transition of hypergraph matchings

We study the problem of approximately counting weighted matchings in hypergraphs of bounded maximum edge size and maximum degree. The problem is expressed as evaluating the partition function, which is the weighted sum of all macthings in a hypergraph where each macthing M is assigned a weight λ|M| in terms of a fixed activity parameter λ. This model unifies two important problems in approximate counting arising from statistical physics: counting independent set (the hardcore model) and counting matchings (the monomer-dimer model).

We show that for hypergraph matchings, the critical activity λc= dd/k(d-1)(d+1) is the uniqueness threshold for the Gibbs measure on (d+1)-uniform (k+1)-regular hyper-tree, and when λ<λc, the hypergraphs of maximum edge size at most d+1 and maximum degree at most k+1 exhibit strong spatial mixing. As a consequence, there is an FPTAS for counting matchings in hypergraphs of bounded maximum edge size at most and bounded maximum degree as long as the uniqueness condition is satisfied.

As for the inapproximability, it can be easily shown that there is no FPRAS for the problem when λ>2λc, unless NP=RP. This left an intriguing gap between λc and 2λc. A key step towards tight inapproximability result is the local weak convergence from a sequence of finite graphs to the extremal measures for the uniqueness threshold on the infinite tree. For hypergraph matchings, we discover that such local weak convergence does not exist for any sequence of finite hypergraphs, which indicates that approximate counting on hypergraphs (or general CSPs) contains much richer structure yet to be understood.

Speaker: Euiwoong Lee

Title: Hardness of Graph Pricing through Generalized Max-Dicut

The Graph Pricing problem is among the fundamental problems whose approximability is not well-understood. While there is a simple combinatorial ¼-approximation algorithm, the best hardness result remains at ½ assuming the Unique Games Conjecture (UGC). We show that it is NP-hard to approximate within a factor better than ¼ under the UGC, so that the simple combinatorial algorithm might be the best possible.

This work is based on the effort to view the Graph Pricing problem as a Constraint Satisfaction Problem (CSP) simpler than the standard and complicated formulation. We propose the problem called Generalized Max-Dicut(T), which has a domain size T + 1 for every T ≥ 1 . Generalized Max-Dicut(1) is well-known Max-Dicut. Besides its connection to Graph Pricing, the hardness of Generalized Max-Dicut is interesting in its own right since in most arity two CSPs studied in the literature, SDP-based algorithms perform better than LP-based or combinatorial algorithms — for this arity two CSP, a simple combinatorial algorithm does the best.

Speaker: Sewoong Oh

Title: Data processing inequality for differential privacy and applications

We provide a hypothesis testing interpretation to differential privacy and derive natural forward and reverse data processing inequalities. These inequalities are very useful in deriving tight impossibility results, as demonstrated by the following two applications: composing multiple queries and multi-party computation. The impossibility results hold for arbitrary parameter settings (privacy levels, number of queries, etc) and for both standard and approximate differential privacy settings. Further, these impossibility results cannot be improved upon.

Speaker: Martin Ziegler

Title: Algebraic Complexity Theory and Quantum Logic

Algebraic models of computation (like register machines) make one operation per step regardless of the value processed. They underly algorithms for sorting or in computational geometry. As opposed to bit models, algebraic methods permit to establish non-trivial lower bounds:

We recall Morgenstern’s elegant proof that the fast fourier transform is optimal; then proceed to the structural complexity theory and prove the quantum satisfiability problem complete for NP_R^0: a well-known discrete complexity class between NP and PSPACE. (Joint work with Christian Herrmann.)