Deterministic simulation in LOGSPACE
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Matrix multiplication via arithmetic progressions
Journal of Symbolic Computation - Special issue on computational algebraic complexity
An introduction to computational learning theory
An introduction to computational learning theory
Computational Complexity
Approximate graph coloring by semidefinite programming
Journal of the ACM (JACM)
Randomness-optimal oblivious sampling
Proceedings of the workshop on Randomized algorithms and computation
The space complexity of approximating the frequency moments
Journal of Computer and System Sciences
Derandomizing Approximation Algorithms Based on Semidefinite Programming
SIAM Journal on Computing
The discrepancy method: randomness and complexity
The discrepancy method: randomness and complexity
Algorithmic derandomization via complexity theory
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Foundations of Cryptography: Basic Tools
Foundations of Cryptography: Basic Tools
Derandomized dimensionality reduction with applications
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
An elementary proof of a theorem of Johnson and Lindenstrauss
Random Structures & Algorithms
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Journal of Computer and System Sciences - Special issu on PODS 2001
Pairwise independence and derandomization
Foundations and Trends® in Theoretical Computer Science
Uncertainty principles, extractors, and explicit embeddings of l2 into l1
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
On variants of the Johnson–Lindenstrauss lemma
Random Structures & Algorithms
Numerical linear algebra in the streaming model
Proceedings of the forty-first annual ACM symposium on Theory of computing
Unbalanced expanders and randomness extractors from Parvaresh--Vardy codes
Journal of the ACM (JACM)
Computational Complexity: A Modern Approach
Computational Complexity: A Modern Approach
The Fast Johnson-Lindenstrauss Transform and Approximate Nearest Neighbors
SIAM Journal on Computing
Fast Dimension Reduction Using Rademacher Series on Dual BCH Codes
Discrete & Computational Geometry
The Johnson–Lindenstrauss Lemma Almost Characterizes Hilbert Space, But Not Quite
Discrete & Computational Geometry
Pseudorandom generators for polynomial threshold functions
Proceedings of the forty-second ACM symposium on Theory of computing
Bounded Independence Fools Degree-2 Threshold Functions
FOCS '10 Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science
Almost optimal explicit Johnson-Lindenstrauss families
APPROX'11/RANDOM'11 Proceedings of the 14th international workshop and 15th international conference on Approximation, randomization, and combinatorial optimization: algorithms and techniques
Bounded Independence Fools Halfspaces
SIAM Journal on Computing
Explicit Construction of a Small $\epsilon$-Net for Linear Threshold Functions
SIAM Journal on Computing
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We construct a small set of explicit linear transformations mapping $\mathbb{R}^n$ to $\mathbb{R}^t$, where $t=O(\log (\gamma^{-1}) \epsilon^{-2})$, such that the $L_2$ norm of any vector in $\mathbb{R}^n$ is distorted by at most $1\pm \epsilon$ in at least a fraction of $1 - \gamma$ of the transformations in the set. Albeit the tradeoff between the size of the set and the success probability is suboptimal compared with probabilistic arguments, we nevertheless are able to apply our construction to a number of problems. In particular, we use it to construct an $\epsilon$-sample (or pseudorandom generator) for linear threshold functions on $\mathbb{S}^{n-1}$ for $\epsilon = o(1)$. We also use it to construct an $\epsilon$-sample for spherical digons in $\mathbb{S}^{n-1}$ for $\epsilon = o(1)$. This construction leads to an efficient oblivious derandomization of the Goemans-Williamson Max-Cut algorithm and similar approximation algorithms (i.e., we construct a small set of hyperplanes such that for any instance we can choose one of them to generate a good solution). Our technique for constructing an $\epsilon$-sample for linear threshold functions on the sphere is considerably different than previous techniques that rely on $k$-wise independent sample spaces.