Dean Doron, Jack Murtagh, Salil Vadhan, David Zuckerman

We give a deterministic, nearly logarithmic-space algorithm for mild spectral sparsification of undirected graphs. Given a weighted, undirected graph $G$ on $n$ vertices described by a binary string of length $N$, an integer $k\leq \log n$ and an error parameter $\varepsilon > 0$, our algorithm runs in space $\tilde{O}(k\log (N\cdot ... more >>>

Ronen Shaltiel, Jad Silbak

We consider codes for space bounded channels. This is a model for communication under noise that was introduced by Guruswami and Smith (J. ACM 2016) and lies between the Shannon (random) and Hamming (adversarial) models. In this model, a channel is a space bounded procedure that reads the codeword in ... more >>>

Eshan Chattopadhyay, Jyun-Jie Liao

In a seminal work, Nisan (Combinatorica'92) constructed a pseudorandom generator for length $n$ and width $w$ read-once branching programs with seed length $O(\log n\cdot \log(nw)+\log n\cdot\log(1/\varepsilon))$ and error $\varepsilon$. It remains a central question to reduce the seed length to $O(\log (nw/\varepsilon))$, which would prove that $\mathbf{BPL}=\mathbf{L}$. However, there has ... more >>>

Edward Pyne

We construct an explicit $\varepsilon$-hitting set generator (HSG) for regular ordered branching programs of length $n$ and $\textit{unbounded width}$ with a single accept state that has seed length

\[O(\log(n)(\log\log(n)+\log(1/\varepsilon))).\]

Previously, the best known seed length for regular branching programs of width $w$ with a single accept state was ...
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Gil Cohen, Dean Doron, Ori Sberlo

We give a deterministic space-efficient algorithm for approximating powers of stochastic matrices. On input a $w \times w$ stochastic matrix $A$, our algorithm approximates $A^{n}$ in space $\widetilde{O}(\log n + \sqrt{\log n}\cdot \log w)$ to within high accuracy. This improves upon the seminal work by Saks and Zhou (FOCS'95), that ... more >>>

Chin Ho Lee, Edward Pyne, Salil Vadhan

We analyze the Fourier growth, i.e. the $L_1$ Fourier weight at level $k$ (denoted $L_{1,k}$), of read-once regular branching programs.

We prove that every read-once regular branching program $B$ of width $w \in [1,\infty]$ with $s$ accepting states on $n$-bit inputs must have its $L_{1,k}$ bounded by

$$

\min\left\{ ...
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Joshua Cook

Let $\mathbf{TISP}[T, S]$, $\mathbf{BPTISP}[T, S]$, $\mathbf{NTISP}[T, S]$, and $\mathbf{CoNTISP}[T, S]$ be the set of languages recognized by deterministic, randomized, nondeterminsitic, and co-nondeterministic algorithms, respectively, running in time $T$ and space $S$. Let $\mathbf{ITIME}[T_V]$ be the set of languages recognized by an interactive protocol where the verifier runs in time $T_V$. ... more >>>

Gil Cohen, Dean Doron, Ori Sberlo, Amnon Ta-Shma

Matrix powering, and more generally iterated matrix multiplication, is a fundamental linear algebraic primitive with myriad applications in computer science. Of particular interest is the problem's space complexity as it constitutes the main route towards resolving the $\mathbf{BPL}$ vs. $\mathbf{L}$ problem. The seminal work by Saks and Zhou (FOCS 1995) ... more >>>

Aaron (Louie) Putterman, Edward Pyne

We give a deterministic white-box algorithm to estimate the expectation of a read-once branching program of length $n$ and width $w$ in space

$$\tilde{O}\left(\log n+\sqrt{\log n}\cdot\log w\right).$$

In particular, we obtain an almost optimal space $\tilde{O}(\log n)$ derandomization of programs up to width $w=2^{\sqrt{\log n}}$.

Previously, ...
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Edward Pyne

We prove that for every $\alpha \in [1,1.5]$,

$$

\text{BPSPACE}[S]\subseteq \text{TISP}[2^{S^{\alpha}},S^{3-\alpha}]

$$

where $\text{BPSPACE}[S]$ corresponds to randomized space $O(S)$ computation, and $\text{TISP}[T,S]$ to time $poly(T)$, space $O(S)$ computation. Our result smoothly interpolates between the results of (Nisan STOC 1992) and (Saks and Zhou FOCS 1995), which prove $\text{BPSPACE}[S]$ is contained ...
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Dean Doron, Edward Pyne, Roei Tell

We provide compelling evidence for the potential of hardness-vs.-randomness approaches to make progress on the long-standing problem of derandomizing space-bounded computation.

Our first contribution is a derandomization of bounded-space machines from hardness assumptions for classes of uniform deterministic algorithms, for which strong (but non-matching) lower bounds can be unconditionally proved. ... more >>>