71 Search Results for "Karlin, Anna R."


Volume

LIPIcs, Volume 94

9th Innovations in Theoretical Computer Science Conference (ITCS 2018)

ITCS 2018, January 11, 2018, Cambridge, MA, USA

Editors: Anna R. Karlin

Document
Invited Talk
A (Slightly) Improved Approximation Algorithm for the Metric Traveling Salesperson Problem (Invited Talk)

Authors: Anna R. Karlin

Published in: LIPIcs, Volume 261, 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023)


Abstract
We describe recent joint work with Nathan Klein and Shayan Oveis Gharan showing that for any metric TSP instance, the max entropy algorithm studied by [Anna R. Karlin et al., 2021] returns a solution of expected cost at most 3/2-ε times the cost of the optimal solution to the subtour elimination LP and hence is a 3/2-ε approximation for the metric TSP problem. The research discussed comes from [Anna R. Karlin et al., 2021], [Anna R. Karlin et al., 2022] and [Anna R. Karlin et al., 2022].

Cite as

Anna R. Karlin. A (Slightly) Improved Approximation Algorithm for the Metric Traveling Salesperson Problem (Invited Talk). In 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 261, p. 1:1, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)


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@InProceedings{karlin:LIPIcs.ICALP.2023.1,
  author =	{Karlin, Anna R.},
  title =	{{A (Slightly) Improved Approximation Algorithm for the Metric Traveling Salesperson Problem}},
  booktitle =	{50th International Colloquium on Automata, Languages, and Programming (ICALP 2023)},
  pages =	{1:1--1:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-278-5},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{261},
  editor =	{Etessami, Kousha and Feige, Uriel and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2023.1},
  URN =		{urn:nbn:de:0030-drops-180531},
  doi =		{10.4230/LIPIcs.ICALP.2023.1},
  annote =	{Keywords: Traveling Salesperson Problem, approximation algorithm}
}
Document
Matroid Partition Property and the Secretary Problem

Authors: Dorna Abdolazimi, Anna R. Karlin, Nathan Klein, and Shayan Oveis Gharan

Published in: LIPIcs, Volume 251, 14th Innovations in Theoretical Computer Science Conference (ITCS 2023)


Abstract
A matroid M on a set E of elements has the α-partition property, for some α > 0, if it is possible to (randomly) construct a partition matroid 𝒫 on (a subset of) elements of M such that every independent set of 𝒫 is independent in M and for any weight function w:E → ℝ_{≥0}, the expected value of the optimum of the matroid secretary problem on 𝒫 is at least an α-fraction of the optimum on M. We show that the complete binary matroid, B_d on 𝔽₂^d does not satisfy the α-partition property for any constant α > 0 (independent of d). Furthermore, we refute a recent conjecture of [Kristóf Bérczi et al., 2021] by showing the same matroid is 2^d/d-colorable but cannot be reduced to an α 2^d/d-colorable partition matroid for any α that is sublinear in d.

Cite as

Dorna Abdolazimi, Anna R. Karlin, Nathan Klein, and Shayan Oveis Gharan. Matroid Partition Property and the Secretary Problem. In 14th Innovations in Theoretical Computer Science Conference (ITCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 251, pp. 2:1-2:9, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)


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@InProceedings{abdolazimi_et_al:LIPIcs.ITCS.2023.2,
  author =	{Abdolazimi, Dorna and Karlin, Anna R. and Klein, Nathan and Oveis Gharan, Shayan},
  title =	{{Matroid Partition Property and the Secretary Problem}},
  booktitle =	{14th Innovations in Theoretical Computer Science Conference (ITCS 2023)},
  pages =	{2:1--2:9},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-263-1},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{251},
  editor =	{Tauman Kalai, Yael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2023.2},
  URN =		{urn:nbn:de:0030-drops-175051},
  doi =		{10.4230/LIPIcs.ITCS.2023.2},
  annote =	{Keywords: Online algorithms, Matroids, Matroid secretary problem}
}
Document
Barriers for Fast Matrix Multiplication from Irreversibility

Authors: Matthias Christandl, Péter Vrana, and Jeroen Zuiddam

Published in: LIPIcs, Volume 137, 34th Computational Complexity Conference (CCC 2019)


Abstract
Determining the asymptotic algebraic complexity of matrix multiplication, succinctly represented by the matrix multiplication exponent omega, is a central problem in algebraic complexity theory. The best upper bounds on omega, leading to the state-of-the-art omega <= 2.37.., have been obtained via the laser method of Strassen and its generalization by Coppersmith and Winograd. Recent barrier results show limitations for these and related approaches to improve the upper bound on omega. We introduce a new and more general barrier, providing stronger limitations than in previous work. Concretely, we introduce the notion of "irreversibility" of a tensor and we prove (in some precise sense) that any approach that uses an irreversible tensor in an intermediate step (e.g., as a starting tensor in the laser method) cannot give omega = 2. In quantitative terms, we prove that the best upper bound achievable is lower bounded by two times the irreversibility of the intermediate tensor. The quantum functionals and Strassen support functionals give (so far, the best) lower bounds on irreversibility. We provide lower bounds on the irreversibility of key intermediate tensors, including the small and big Coppersmith - Winograd tensors, that improve limitations shown in previous work. Finally, we discuss barriers on the group-theoretic approach in terms of "monomial" irreversibility.

Cite as

Matthias Christandl, Péter Vrana, and Jeroen Zuiddam. Barriers for Fast Matrix Multiplication from Irreversibility. In 34th Computational Complexity Conference (CCC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 137, pp. 26:1-26:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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@InProceedings{christandl_et_al:LIPIcs.CCC.2019.26,
  author =	{Christandl, Matthias and Vrana, P\'{e}ter and Zuiddam, Jeroen},
  title =	{{Barriers for Fast Matrix Multiplication from Irreversibility}},
  booktitle =	{34th Computational Complexity Conference (CCC 2019)},
  pages =	{26:1--26:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-116-0},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{137},
  editor =	{Shpilka, Amir},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CCC.2019.26},
  URN =		{urn:nbn:de:0030-drops-108487},
  doi =		{10.4230/LIPIcs.CCC.2019.26},
  annote =	{Keywords: Matrix multiplication exponent, barriers, laser method}
}
Document
Complete Volume
LIPIcs, Volume 94, ITCS'18, Complete Volume

Authors: Anna R. Karlin

Published in: LIPIcs, Volume 94, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018)


Abstract
LIPIcs, Volume 94, ITCS'18, Complete Volume

Cite as

Anna R. Karlin. LIPIcs, Volume 94, ITCS'18, Complete Volume. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)


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@Proceedings{karlin:LIPIcs.ITCS.2018,
  title =	{{LIPIcs, Volume 94, ITCS'18, Complete Volume}},
  booktitle =	{9th Innovations in Theoretical Computer Science Conference (ITCS 2018)},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-060-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{94},
  editor =	{Karlin, Anna R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2018},
  URN =		{urn:nbn:de:0030-drops-84419},
  doi =		{10.4230/LIPIcs.ITCS.2018},
  annote =	{Keywords: Theory of Computation, Mathematics of Computing}
}
Document
Front Matter
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Anna R. Karlin

Published in: LIPIcs, Volume 94, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018)


Abstract
Front Matter, Table of Contents, Preface, Conference Organization

Cite as

Anna R. Karlin. Front Matter, Table of Contents, Preface, Conference Organization. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, pp. 0:i-0:xii, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)


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@InProceedings{karlin:LIPIcs.ITCS.2018.0,
  author =	{Karlin, Anna R.},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{9th Innovations in Theoretical Computer Science Conference (ITCS 2018)},
  pages =	{0:i--0:xii},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-060-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{94},
  editor =	{Karlin, Anna R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2018.0},
  URN =		{urn:nbn:de:0030-drops-83104},
  doi =		{10.4230/LIPIcs.ITCS.2018.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Barriers for Rank Methods in Arithmetic Complexity

Authors: Klim Efremenko, Ankit Garg, Rafael Oliveira, and Avi Wigderson

Published in: LIPIcs, Volume 94, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018)


Abstract
Arithmetic complexity, the study of the cost of computing polynomials via additions and multiplications, is considered (for many good reasons) simpler to understand than Boolean complexity, namely computing Boolean functions via logical gates. And indeed, we seem to have significantly more lower bound techniques and results in arithmetic complexity than in Boolean complexity. Despite many successes and rapid progress, however, foundational challenges, like proving super-polynomial lower bounds on circuit or formula size for explicit polynomials, or super-linear lower bounds on explicit 3-dimensional tensors, remain elusive. At the same time (and possibly for similar reasons), we have plenty more excuses, in the form of "barrier results" for failing to prove basic lower bounds in Boolean complexity than in arithmetic complexity. Efforts to find barriers to arithmetic lower bound techniques seem harder, and despite some attempts we have no excuses of similar quality for these failures in arithmetic complexity. This paper aims to add to this study. In this paper we address rank methods, which were long recognized as encompassing and abstracting almost all known arithmetic lower bounds to-date, including the most recent impressive successes. Rank methods (under the name of flattenings) are also in wide use in algebraic geometry for proving tensor rank and symmetric tensor rank lower bounds. Our main results are barriers to these methods. In particular, 1. Rank methods cannot prove better than (2^d)*n^(d/2) lower bound on the tensor rank of any d-dimensional tensor of side n. (In particular, they cannot prove super-linear, indeed even >8n tensor rank lower bounds for any 3-dimensional tensors.) 2. Rank methods cannot prove (d+1)n^(d/2) on the Waring rank of any n-variate polynomial of degree d. (In particular, they cannot prove such lower bounds on stronger models, including depth-3 circuits.) The proofs of these bounds use simple linear-algebraic arguments, leveraging connections between the symbolic rank of matrix polynomials and the usual rank of their evaluations. These techniques can perhaps be extended to barriers for other arithmetic models on which progress has halted. To see how these barrier results directly inform the state-of-art in arithmetic complexity we note the following. First, the bounds above nearly match the best explicit bounds we know for these models, hence offer an explanations why the rank methods got stuck there. Second, the bounds above are a far cry (quadratically away) from the true complexity (e.g. of random polynomials) in these models, which if achieved (by any methods), are known to imply super-polynomial formula lower bounds. We also explain the relation of our barrier results to other attempts, and in particular how they significantly differ from the recent attempts to find analogues of "natural proofs" for arithmetic complexity. Finally, we discuss the few arithmetic lower bound approaches which fall outside rank methods, and some natural directions our barriers suggest.

Cite as

Klim Efremenko, Ankit Garg, Rafael Oliveira, and Avi Wigderson. Barriers for Rank Methods in Arithmetic Complexity. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, pp. 1:1-1:19, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)


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@InProceedings{efremenko_et_al:LIPIcs.ITCS.2018.1,
  author =	{Efremenko, Klim and Garg, Ankit and Oliveira, Rafael and Wigderson, Avi},
  title =	{{Barriers for Rank Methods in Arithmetic Complexity}},
  booktitle =	{9th Innovations in Theoretical Computer Science Conference (ITCS 2018)},
  pages =	{1:1--1:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-060-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{94},
  editor =	{Karlin, Anna R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2018.1},
  URN =		{urn:nbn:de:0030-drops-83506},
  doi =		{10.4230/LIPIcs.ITCS.2018.1},
  annote =	{Keywords: Lower Bounds, Barriers, Partial Derivatives, Flattenings, Algebraic Complexity}
}
Document
A Complexity Trichotomy for k-Regular Asymmetric Spin Systems Using Number Theory

Authors: Jin-Yi Cai, Zhiguo Fu, Kurt Girstmair, and Michael Kowalczyk

Published in: LIPIcs, Volume 94, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018)


Abstract
Suppose \varphi and \psi are two angles satisfying \tan(\varphi) = 2 \tan(\psi) > 0. We prove that under this condition \varphi and \psi cannot be both rational multiples of \pi. We use this number theoretic result to prove a classification of the computational complexity of spin systems on k-regular graphs with general (not necessarily symmetric) real valued edge weights. We establish explicit criteria, according to which the partition functions of all such systems are classified into three classes: (1) Polynomial time computable, (2) \#P-hard in general but polynomial time computable on planar graphs, and (3) \#P-hard on planar graphs. In particular problems in (2) are precisely those that can be transformed to a form solvable by the Fisher-Kasteleyn-Temperley algorithm by a holographic reduction.

Cite as

Jin-Yi Cai, Zhiguo Fu, Kurt Girstmair, and Michael Kowalczyk. A Complexity Trichotomy for k-Regular Asymmetric Spin Systems Using Number Theory. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, pp. 2:1-2:22, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)


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@InProceedings{cai_et_al:LIPIcs.ITCS.2018.2,
  author =	{Cai, Jin-Yi and Fu, Zhiguo and Girstmair, Kurt and Kowalczyk, Michael},
  title =	{{A Complexity Trichotomy for k-Regular Asymmetric Spin Systems Using Number Theory}},
  booktitle =	{9th Innovations in Theoretical Computer Science Conference (ITCS 2018)},
  pages =	{2:1--2:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-060-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{94},
  editor =	{Karlin, Anna R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2018.2},
  URN =		{urn:nbn:de:0030-drops-83251},
  doi =		{10.4230/LIPIcs.ITCS.2018.2},
  annote =	{Keywords: Spin Systems, Holant Problems, Number Theory, Characters, Cyclotomic Fields}
}
Document
Quantum Query Algorithms are Completely Bounded Forms

Authors: Srinivasan Arunachalam, Jop Briët, and Carlos Palazuelos

Published in: LIPIcs, Volume 94, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018)


Abstract
We prove a characterization of quantum query algorithms in terms of polynomials satisfying a certain (completely bounded) norm constraint. Based on this, we obtain a refined notion of approximate polynomial degree that equals the quantum query complexity, answering a question of Aaronson et al. (CCC'16). Using this characterization, we show that many polynomials of degree at least 4 are far from those coming from quantum query algorithms. Our proof is based on a fundamental result of Christensen and Sinclair (J. Funct. Anal., 1987) that generalizes the well-known Stinespring representation for quantum channels to multilinear forms. We also give a simple and short proof of one of the results of Aaronson et al. showing an equivalence between one-query quantum algorithms and bounded quadratic polynomials.

Cite as

Srinivasan Arunachalam, Jop Briët, and Carlos Palazuelos. Quantum Query Algorithms are Completely Bounded Forms. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, pp. 3:1-3:21, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)


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@InProceedings{arunachalam_et_al:LIPIcs.ITCS.2018.3,
  author =	{Arunachalam, Srinivasan and Bri\"{e}t, Jop and Palazuelos, Carlos},
  title =	{{Quantum Query Algorithms are Completely Bounded Forms}},
  booktitle =	{9th Innovations in Theoretical Computer Science Conference (ITCS 2018)},
  pages =	{3:1--3:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-060-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{94},
  editor =	{Karlin, Anna R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2018.3},
  URN =		{urn:nbn:de:0030-drops-83383},
  doi =		{10.4230/LIPIcs.ITCS.2018.3},
  annote =	{Keywords: Quantum query algorithms, operator space theory, polynomial method, approximate degree.}
}
Document
A Complete Characterization of Unitary Quantum Space

Authors: Bill Fefferman and Cedric Yen-Yu Lin

Published in: LIPIcs, Volume 94, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018)


Abstract
Motivated by understanding the power of quantum computation with restricted number of qubits, we give two complete characterizations of unitary quantum space bounded computation. First we show that approximating an element of the inverse of a well-conditioned efficiently encoded 2^k(n) x 2^k(n) matrix is complete for the class of problems solvable by quantum circuits acting on O(k(n)) qubits with all measurements at the end of the computation. Similarly, estimating the minimum eigenvalue of an efficiently encoded Hermitian 2^k(n) x 2^k(n) matrix is also complete for this class. In the logspace case, our results improve on previous results of Ta-Shma by giving new space-efficient quantum algorithms that avoid intermediate measurements, as well as showing matching hardness results. Additionally, as a consequence we show that preciseQMA, the version of QMA with exponentially small completeness-soundess gap, is equal to PSPACE. Thus, the problem of estimating the minimum eigenvalue of a local Hamiltonian to inverse exponential precision is PSPACE-complete, which we show holds even in the frustration-free case. Finally, we can use this characterization to give a provable setting in which the ability to prepare the ground state of a local Hamiltonian is more powerful than the ability to prepare PEPS states. Interestingly, by suitably changing the parameterization of either of these problems we can completely characterize the power of quantum computation with simultaneously bounded time and space.

Cite as

Bill Fefferman and Cedric Yen-Yu Lin. A Complete Characterization of Unitary Quantum Space. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, pp. 4:1-4:21, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)


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@InProceedings{fefferman_et_al:LIPIcs.ITCS.2018.4,
  author =	{Fefferman, Bill and Lin, Cedric Yen-Yu},
  title =	{{A Complete Characterization of Unitary Quantum Space}},
  booktitle =	{9th Innovations in Theoretical Computer Science Conference (ITCS 2018)},
  pages =	{4:1--4:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-060-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{94},
  editor =	{Karlin, Anna R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2018.4},
  URN =		{urn:nbn:de:0030-drops-83242},
  doi =		{10.4230/LIPIcs.ITCS.2018.4},
  annote =	{Keywords: Quantum complexity, space complexity, complete problems, QMA}
}
Document
Matrix Completion and Related Problems via Strong Duality

Authors: Maria-Florina Balcan, Yingyu Liang, David P. Woodruff, and Hongyang Zhang

Published in: LIPIcs, Volume 94, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018)


Abstract
This work studies the strong duality of non-convex matrix factorization problems: we show that under certain dual conditions, these problems and its dual have the same optimum. This has been well understood for convex optimization, but little was known for non-convex problems. We propose a novel analytical framework and show that under certain dual conditions, the optimal solution of the matrix factorization program is the same as its bi-dual and thus the global optimality of the non-convex program can be achieved by solving its bi-dual which is convex. These dual conditions are satisfied by a wide class of matrix factorization problems, although matrix factorization problems are hard to solve in full generality. This analytical framework may be of independent interest to non-convex optimization more broadly. We apply our framework to two prototypical matrix factorization problems: matrix completion and robust Principal Component Analysis (PCA). These are examples of efficiently recovering a hidden matrix given limited reliable observations of it. Our framework shows that exact recoverability and strong duality hold with nearly-optimal sample complexity guarantees for matrix completion and robust PCA.

Cite as

Maria-Florina Balcan, Yingyu Liang, David P. Woodruff, and Hongyang Zhang. Matrix Completion and Related Problems via Strong Duality. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, pp. 5:1-5:22, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)


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@InProceedings{balcan_et_al:LIPIcs.ITCS.2018.5,
  author =	{Balcan, Maria-Florina and Liang, Yingyu and Woodruff, David P. and Zhang, Hongyang},
  title =	{{Matrix Completion and Related Problems via Strong Duality}},
  booktitle =	{9th Innovations in Theoretical Computer Science Conference (ITCS 2018)},
  pages =	{5:1--5:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-060-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{94},
  editor =	{Karlin, Anna R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2018.5},
  URN =		{urn:nbn:de:0030-drops-83583},
  doi =		{10.4230/LIPIcs.ITCS.2018.5},
  annote =	{Keywords: Non-Convex Optimization, Strong Duality, Matrix Completion, Robust PCA, Sample Complexity}
}
Document
A Quasi-Random Approach to Matrix Spectral Analysis

Authors: Michael Ben-Or and Lior Eldar

Published in: LIPIcs, Volume 94, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018)


Abstract
Inspired by quantum computing algorithms for Linear Algebra problems [Harrow et al., Phys. Rev. Lett. 2009, Ta-Shma, STOC 2013] we study how simulation on a classical computer of this type of "Phase Estimation algorithms" performs when we apply it to the Eigen-Problem of Hermitian matrices. The result is a completely new, efficient and stable, parallel algorithm to compute an approximate spectral decomposition of any Hermitian matrix. The algorithm can be implemented by Boolean circuits in O(log^2(n)) parallel time with a total cost of O(n^(\omega+1)) Boolean operations. This Boolean complexity matches the best known O(log^2(n)) parallel time algorithms, but unlike those algorithms our algorithm is (logarithmically) stable, so it may lead to actual implementations, allowing fast parallel computation of eigenvectors and eigenvalues in practice. Previous approaches to solve the Eigen-Problem generally use randomization to avoid bad conditions - as we do. Our algorithm makes further use of randomization in a completely new way, taking random powers of a unitary matrix to randomize the phases of its eigenvalues. Proving that a tiny Gaussian perturbation and a random polynomial power are sufficient to ensure almost pairwise independence of the phases (mod 2pi) is the main technical contribution of this work. It relies on the theory of low-discrepancy or quasi-random sequences - a theory, which to the best of our knowledge, has not been connected thus far to linear algebra problems. Hence, we believe that further study of this new connection will lead to additional improvements.

Cite as

Michael Ben-Or and Lior Eldar. A Quasi-Random Approach to Matrix Spectral Analysis. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, pp. 6:1-6:22, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)


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@InProceedings{benor_et_al:LIPIcs.ITCS.2018.6,
  author =	{Ben-Or, Michael and Eldar, Lior},
  title =	{{A Quasi-Random Approach to Matrix Spectral Analysis}},
  booktitle =	{9th Innovations in Theoretical Computer Science Conference (ITCS 2018)},
  pages =	{6:1--6:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-060-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{94},
  editor =	{Karlin, Anna R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2018.6},
  URN =		{urn:nbn:de:0030-drops-83288},
  doi =		{10.4230/LIPIcs.ITCS.2018.6},
  annote =	{Keywords: linear algebra, eigenvalues, eigenvectors, low-discrepancy sequence}
}
Document
Non-Negative Sparse Regression and Column Subset Selection with L1 Error

Authors: Aditya Bhaskara and Silvio Lattanzi

Published in: LIPIcs, Volume 94, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018)


Abstract
We consider the problems of sparse regression and column subset selection under L1 error. For both problems, we show that in the non-negative setting it is possible to obtain tight and efficient approximations, without any additional structural assumptions (such as restricted isometry, incoherence, expansion, etc.). For sparse regression, given a matrix A and a vector b with non-negative entries, we give an efficient algorithm to output a vector x of sparsity O(k), for which |Ax - b|_1 is comparable to the smallest error possible using non-negative k-sparse x. We then use this technique to obtain our main result: an efficient algorithm for column subset selection under L1 error for non-negative matrices.

Cite as

Aditya Bhaskara and Silvio Lattanzi. Non-Negative Sparse Regression and Column Subset Selection with L1 Error. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, pp. 7:1-7:15, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)


Copy BibTex To Clipboard

@InProceedings{bhaskara_et_al:LIPIcs.ITCS.2018.7,
  author =	{Bhaskara, Aditya and Lattanzi, Silvio},
  title =	{{Non-Negative Sparse Regression and Column Subset Selection with L1 Error}},
  booktitle =	{9th Innovations in Theoretical Computer Science Conference (ITCS 2018)},
  pages =	{7:1--7:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-060-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{94},
  editor =	{Karlin, Anna R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2018.7},
  URN =		{urn:nbn:de:0030-drops-83548},
  doi =		{10.4230/LIPIcs.ITCS.2018.7},
  annote =	{Keywords: Sparse regression, L1 error optimization, Column subset selection}
}
Document
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness

Authors: Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff

Published in: LIPIcs, Volume 94, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018)


Abstract
Understanding the singular value spectrum of an n x n matrix A is a fundamental task in countless numerical computation and data analysis applications. In matrix multiplication time, it is possible to perform a full SVD of A and directly compute the singular values \sigma_1,...,\sigma_n. However, little is known about algorithms that break this runtime barrier. Using tools from stochastic trace estimation, polynomial approximation, and fast linear system solvers, we show how to efficiently isolate different ranges of A's spectrum and approximate the number of singular values in these ranges. We thus effectively compute an approximate histogram of the spectrum, which can stand in for the true singular values in many applications. We use our histogram primitive to give the first algorithms for approximating a wide class of symmetric matrix norms and spectral sums faster than the best known runtime for matrix multiplication. For example, we show how to obtain a (1 + \epsilon) approximation to the Schatten 1-norm (i.e. the nuclear or trace norm) in just ~ O((nnz(A)n^{1/3} + n^2)\epsilon^{-3}) time for A with uniform row sparsity or \tilde O(n^{2.18} \epsilon^{-3}) time for dense matrices. The runtime scales smoothly for general Schatten-p norms, notably becoming \tilde O (p nnz(A) \epsilon^{-3}) for any real p >= 2. At the same time, we show that the complexity of spectrum approximation is inherently tied to fast matrix multiplication in the small \epsilon regime. We use fine-grained complexity to give conditional lower bounds for spectrum approximation, showing that achieving milder \epsilon dependencies in our algorithms would imply triangle detection algorithms for general graphs running in faster than state of the art matrix multiplication time. This further implies, through a reduction of (Williams & William, 2010), that highly accurate spectrum approximation algorithms running in subcubic time can be used to give subcubic time matrix multiplication. As an application of our bounds, we show that precisely computing all effective resistances in a graph in less than matrix multiplication time is likely difficult, barring a major algorithmic breakthrough.

Cite as

Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, pp. 8:1-8:21, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)


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@InProceedings{musco_et_al:LIPIcs.ITCS.2018.8,
  author =	{Musco, Cameron and Netrapalli, Praneeth and Sidford, Aaron and Ubaru, Shashanka and Woodruff, David P.},
  title =	{{Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness}},
  booktitle =	{9th Innovations in Theoretical Computer Science Conference (ITCS 2018)},
  pages =	{8:1--8:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-060-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{94},
  editor =	{Karlin, Anna R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2018.8},
  URN =		{urn:nbn:de:0030-drops-83397},
  doi =		{10.4230/LIPIcs.ITCS.2018.8},
  annote =	{Keywords: spectrum approximation, matrix norm computation, fine-grained complexity, linear algebra}
}
Document
Size, Cost and Capacity: A Semantic Technique for Hard Random QBFs

Authors: Olaf Beyersdorff, Joshua Blinkhorn, and Luke Hinde

Published in: LIPIcs, Volume 94, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018)


Abstract
As a natural extension of the SAT problem, an array of proof systems for quantified Boolean formulas (QBF) have been proposed, many of which extend a propositional proof system to handle universal quantification. By formalising the construction of the QBF proof system obtained from a propositional proof system by adding universal reduction (Beyersdorff, Bonacina & Chew, ITCS'16), we present a new technique for proving proof-size lower bounds in these systems. The technique relies only on two semantic measures: the cost of a QBF, and the capacity of a proof. By examining the capacity of proofs in several QBF systems, we are able to use the technique to obtain lower bounds based on cost alone. As applications of the technique, we first prove exponential lower bounds for a new family of simple QBFs representing equality. The main application is in proving exponential lower bounds with high probability for a class of randomly generated QBFs, the first 'genuine' lower bounds of this kind, which apply to the QBF analogues of resolution, Cutting Planes, and Polynomial Calculus. Finally, we employ the technique to give a simple proof of hardness for a prominent family of QBFs.

Cite as

Olaf Beyersdorff, Joshua Blinkhorn, and Luke Hinde. Size, Cost and Capacity: A Semantic Technique for Hard Random QBFs. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, pp. 9:1-9:18, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)


Copy BibTex To Clipboard

@InProceedings{beyersdorff_et_al:LIPIcs.ITCS.2018.9,
  author =	{Beyersdorff, Olaf and Blinkhorn, Joshua and Hinde, Luke},
  title =	{{Size, Cost and Capacity: A Semantic Technique for Hard Random QBFs}},
  booktitle =	{9th Innovations in Theoretical Computer Science Conference (ITCS 2018)},
  pages =	{9:1--9:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-060-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{94},
  editor =	{Karlin, Anna R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2018.9},
  URN =		{urn:nbn:de:0030-drops-83228},
  doi =		{10.4230/LIPIcs.ITCS.2018.9},
  annote =	{Keywords: quantified Boolean formulas, proof complexity, lower bounds}
}
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