Arrow Research search

Author name cluster

Troy Lee

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers
2 author rows

Possible papers

11

FOCS Conference 2022 Conference Paper

Cut Query Algorithms with Star Contraction

  • Simon Apers
  • Yuval Efron
  • Pawel Gawrychowski
  • Troy Lee
  • Sagnik Mukhopadhyay
  • Danupon Nanongkai

We study the complexity of determining the edge connectivity of a simple graph with cut queries. We show that (i) there is a bounded-error randomized algorithm that computes edge connectivity with $O(n)$ cut queries, and (ii) there is a bounded-error quantum algorithm that computes edge connectivity with $\tilde{O}(\sqrt{}$n) cut queries. To prove these results we introduce a new technique, called star contraction, to randomly contract edges of a graph while preserving non-trivial minimum cuts. In star contraction vertices randomly contract an edge incident on a small set of randomly chosen “center” vertices. In contrast to the related 2-out contraction technique of Ghaffari, Nowicki, and Thorup [SODA’20], star contraction only contracts vertex-disjoint star subgraphs, which allows it to be efficiently implemented via cut queries. The $O(n)$ bound from item (i) was not known even for the simpler problem of connectivity, and it improves the $O(n\log^{3}n)$ upper bound by Rubinstein, Schramm, and Weinberg [ITCS’18]. The bound is tight under the reasonable conjecture that the randomized communication complexity of connectivity is $\Omega(n\log n)$, an open question since the seminal work of Babai, Frankl, and Simon [FOCS’86]. The bound also excludes using edge connectivity on simple graphs to prove a superlinear randomized query lower bound for minimizing a symmetric submodular function. The quantum algorithm from item (ii) gives a nearlyquadratic separation with the randomized complexity, and addresses an open question of Lee, Santha, and Zhang [SODA’21]. The algorithm can alternatively be viewed as computing the edge connectivity of a simple graph with $\tilde{O}(\sqrt{}$n) matrix-vector multiplication queries to its adjacency matrix. Finally, we demonstrate the use of star contraction outside of the cut query setting by designing a one-pass semi-streaming algorithm for computing edge connectivity in the complete vertex arrival setting. This contrasts with the edge arrival setting where two passes are required.

SODA Conference 2021 Conference Paper

Quantum algorithms for graph problems with cut queries

  • Troy Lee
  • Miklos Santha
  • Shengyu Zhang 0002

Let G be an n -vertex graph with m edges. When asked a subset S of vertices, a cut query on G returns the number of edges of G that have exactly one endpoint in S. We show that there is a bounded-error quantum algorithm that determines all connected components of G after making O (log( n ) 6 ) many cut queries. In contrast, it follows from results in communication complexity that any randomized algorithm even just to decide whether the graph is connected or not must make at least Ω( n/ log( n )) many cut queries. We further show that with O (log( n ) 8 ) many cut queries a quantum algorithm can with high probability output a spanning forest for G. En route to proving these results, we design quantum algorithms for learning a graph using cut queries. We show that a quantum algorithm can learn a graph with maximum degree d after O ( d log( n ) 2 ) many cut queries, and can learn a general graph with many cut queries. These two upper bounds are tight up to the poly-logarithmic factors, and compare to Ω( dn ) and Ω( m/ log( n )) lower bounds on the number of cut queries needed by a randomized algorithm for the same problems, respectively. The key ingredients in our results are the Bernstein-Vazirani algorithm, approximate counting with “OR queries”, and learning sparse vectors from inner products as in compressed sensing.

FOCS Conference 2016 Conference Paper

Separations in Communication Complexity Using Cheat Sheets and Information Complexity

  • Anurag Anshu
  • Aleksandrs Belovs
  • Shalev Ben-David
  • Mika Göös
  • Rahul Jain 0001
  • Robin Kothari
  • Troy Lee
  • Miklos Santha

While exponential separations are known between quantum and randomized communication complexity for partial functions (Raz, STOC 1999), the best known separation between these measures for a total function is quadratic, witnessed by the disjointness function. We give the first super-quadratic separation between quantum and randomized communication complexity for a total function, giving an example exhibiting a power 2. 5 gap. We further present a 1. 5 power separation between exact quantum and randomized communication complexity, improving on the previous ≅ 1. 15 separation by Ambainis (STOC 2013). Finally, we present a nearly optimal quadratic separation between randomized communication complexity and the logarithm of the partition number, improving upon the previous best power 1. 5 separation due to Goos, Jayram, Pitassi, and Watson. Our results are the communication analogues of separations in query complexity proved using the recent cheat sheet framework of Aaronson, Ben-David, and Kothari (STOC 2016). Our main technical results are randomized communication and information complexity lower bounds for a family of functions, called lookup functions, that generalize and port the cheat sheet framework to communication complexity.

NeurIPS Conference 2013 Conference Paper

Matrix Completion From any Given Set of Observations

  • Troy Lee
  • Adi Shraibman

In the matrix completion problem the aim is to recover an unknown real matrix from a subset of its entries. This problem comes up in many application areas, and has received a great deal of attention in the context of the netflix prize. A central approach to this problem is to output a matrix of lowest possible complexity (e. g. rank or trace norm) that agrees with the partially specified matrix. The performance of this approach under the assumption that the revealed entries are sampled randomly has received considerable attention. In practice, often the set of revealed entries is not chosen at random and these results do not apply. We are therefore left with no guarantees on the performance of the algorithm we are using. We present a means to obtain performance guarantees with respect to any set of initial observations. The first step remains the same: find a matrix of lowest possible complexity that agrees with the partially specified matrix. We give a new way to interpret the output of this algorithm by next finding a probability distribution over the non-revealed entries with respect to which a bound on the generalization error can be proven. The more complex the set of revealed entries according to a certain measure, the better the bound on the generalization error.

STOC Conference 2013 Conference Paper

The approximate rank of a matrix and its algorithmic applications: approximate rank

  • Noga Alon
  • Troy Lee
  • Adi Shraibman
  • Santosh S. Vempala

We study the ε-rank of a real matrix A, defined for any ε > 0 as the minimum rank over matrices that approximate every entry of A to within an additive ε. This parameter is connected to other notions of approximate rank and is motivated by problems from various topics including communication complexity, combinatorial optimization, game theory, computational geometry and learning theory. Here we give bounds on the ε-rank and use them for algorithmic applications. Our main algorithmic results are (a) polynomial-time additive approximation schemes for Nash equilibria for 2-player games when the payoff matrices are positive semidefinite or have logarithmic rank and (b) an additive PTAS for the densest subgraph problem for similar classes of weighted graphs. We use combinatorial, geometric and spectral techniques; our main new tool is an algorithm for efficiently covering a convex body with translates of another convex body.

FOCS Conference 2011 Conference Paper

Quantum Query Complexity of State Conversion

  • Troy Lee
  • Rajat Mittal 0001
  • Ben Reichardt
  • Robert Spalek
  • Mario Szegedy

State conversion generalizes query complexity to the problem of converting between two input-dependent quantum states by making queries to the input. We characterize the complexity of this problem by introducing a natural information-theoretic norm that extends the Schur product operator norm. The complexity of converting between two systems of states is given by the distance between them, as measured by this norm. In the special case of function evaluation, the norm is closely related to the general adversary bound, a semi-definite program that lower-bounds the number of input queries needed by a quantum algorithm to evaluate a function. We thus obtain that the general adversary bound characterizes the quantum query complexity of any function whatsoever. This generalizes and simplifies the proof of the same result in the case of boolean input and output. Also in the case of function evaluation, we show that our norm satisfies a remarkable composition property, implying that the quantum query complexity of the composition of two functions is at most the product of the query complexities of the functions, up to a constant. Finally, our result implies that discrete and continuous-time query models are equivalent in the bounded-error setting, even for the general state-conversion problem.

TCS Journal 2005 Journal Article

Resource bounded symmetry of information revisited

  • Troy Lee
  • Andrei Romashchenko

The information contained in a string x about a string y is the difference between the Kolmogorov complexity of y and the conditional Kolmogorov complexity of y given x, i. e. , I ( x: y ) = C ( y ) - C ( y | x ). The Kolmogorov–Levin Theorem says that I ( x: y ) is symmetric up to a small additive term. We investigate if this property also holds for several versions of polynomial time-bounded Kolmogorov complexity. We study symmetry of information for some variants of distinguishing complexity CD where CD ( x ) is the length of a shortest program which accepts x and only x. We show relativized worlds where symmetry of information does not hold in a strong way for deterministic and nondeterministic polynomial time distinguishing complexities CD poly and CND poly. On the other hand, for nondeterministic polynomial time distinguishing complexity with randomness, CAMD poly, we show that symmetry of information holds for most pairs of strings in any set in NP. Our techniques extend work of Buhrman et al. (Language compression and pseudorandom generators, in: Proc. 19th IEEE Conf. on Computational Complexity, IEEE, New York, 2004, pp. 15–28) on language compression by AM algorithms, and have the following application to the compression of samplable sources, introduced in Trevisan et al. (Compression of sample sources, in: Proc. 19th IEEE Conf. on Computational Complexity, IEEE, New York, 2004, pp. 1–15): any element x in the support of a polynomial time samplable source X can be given a description of size - log Pr [ X = x ] + O ( log 3 n ), from which x can be recovered by an AM algorithm.

MFCS Conference 2004 Conference Paper

On Polynomially Time Bounded Symmetry of Information

  • Troy Lee
  • Andrei Romashchenko

Abstract The information contained in a string x about a string y is defined as the difference between the Kolmogorov complexity of y and the conditional Kolmogorov complexity of y given x, i. e. , I ( x: y )=C( y )–C( y | x ). From the well-known Kolmogorov–Levin Theorem it follows that I ( x: y ) is symmetric up to a small additive term O (logC( x, y )). We investigate if this property can hold for several versions of polynomial time bounded Kolmogorov complexity. In particular, we study symmetry of information for some variants of distinguishing complexity CD where CD( x ) is the length of a shortest program which accepts x and only x. We show relativized worlds where symmetry of information does not hold for deterministic and nondeterministic polynomial time distinguishing complexities CD poly and CND poly For nondeterministic polynomial time distinguishing with randomness, CAM poly, we prove that symmetry of information holds for most pairs of strings in any set in NP. In proving this last statement we extend a recent result of Buhrman et al. [6], which may be of independent utility.