Amos Beimel, Anna Gal, Michael S. Paterson

The model of span programs is a linear algebraic model of

computation. Lower bounds for span programs imply lower bounds for

contact schemes, symmetric branching programs and for formula size.

Monotone span programs correspond also to linear secret-sharing schemes.

We present a new technique for proving lower bounds for ...
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Martin Sauerhoff

In this paper, we are concerned with randomized OBDDs and randomized

read-k-times branching programs. We present an example of a Boolean

function which has polynomial size randomized OBDDs with small,

one-sided error, but only non-deterministic read-once branching

programs of exponential size. Furthermore, we discuss a lower bound

technique for randomized ...
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Miklos Ajtai

Our computational model is a random access machine with $n$

read only input registers each containing $ c \log n$ bits of

information and a read and write memory. We measure the time by the

number of accesses to the input registers. We show that for all $k$

there is ...
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Miklos Ajtai

We prove that for all positive integer $k$ and for all

sufficiently small $\epsilon >0$ if $n$ is sufficiently large

then there is no Boolean (or $2$-way) branching program of size

less than $2^{\epsilon n}$ which for all inputs

$X\subseteq \lbrace 0,1,...,n-1\rbrace $ computes in time $kn$

the parity of ...
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E.A. Okol'nishnikiva

Some operations over Boolean functions are considered. It is shown that

the operation of the geometrical projection and the operation of the

monotone extension can increase the complexity of Boolean functions for

formulas in each finite basis, for switching networks, for branching

programs, and read-$k$-times ...
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Petr Savicky, Detlef Sieling

Restricted branching programs are considered in complexity theory in

order to study the space complexity of sequential computations and

in applications as a data structure for Boolean functions. In this

paper (\oplus,k)-branching programs and (\vee,k)-branching

programs are considered, i.e., branching programs starting with a

...
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Rustam Mubarakzjanov

Restricted branching programs are considered by the investigation

of relationships between complexity classes of Boolean functions.

Read-once ordered branching programs (or OBDDs) form the most restricted class

of this computation model.

Since the problem of proving exponential lower bounds on the complexity

for general probabilistic OBDDs is open so ...
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Stasys Jukna, Stanislav Zak

We propose an information-theoretic approach to proving lower

bounds on the size of branching programs. The argument is based on

Kraft-McMillan type inequalities for the average amount of

uncertainty about (or entropy of) a given input during the various

stages of computation. The uncertainty is measured by the average

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Jui-Lin Lee

In this paper we give a direct proof of $N_0=N_0^\prime$, i.e., the equivalence of

uniform $NC^1$ based on different recursion principles: one is OR-AND complete

binary tree (in depth $\log n$) and the other is the recursion on notation with value

bounded in $[0,k]$ and $|x|(=n)$ many ...
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Stasys Jukna

We consider so-called ``incremental'' dynamic programming (DP) algorithms, and are interested in the number of subproblems produced by them. The standard DP algorithm for the n-dimensional Knapsack problem is incremental, and produces nK subproblems, where K is the capacity of the knapsack. We show that any incremental algorithm for this ... more >>>

Emanuele Viola

We draw two incomplete, biased maps of challenges in

computational complexity lower bounds. Our aim is to put

these challenges in perspective, and to present some

connections which do not seem widely known.

Craig Gentry

We show that, for many noncommutative algebras A, the hardness of computing the determinant of matrices over A follows almost immediately from Barrington’s Theorem. Barrington showed how to express a NC1 circuit as a product program over any non-solvable group. We construct a simple matrix directly from Barrington’s product program ... more >>>

Ran Raz

We prove a general time-space lower bound that applies for a large class of learning problems and shows that for every problem in that class, any learning algorithm requires either a memory of quadratic size or an exponential number of samples.

Our result is stated in terms of the norm ... more >>>

Sumegha Garg, Ran Raz, Avishay Tal

A matrix $M: A \times X \rightarrow \{-1,1\}$ corresponds to the following learning problem: An unknown element $x \in X$ is chosen uniformly at random. A learner tries to learn $x$ from a stream of samples, $(a_1, b_1), (a_2, b_2) \ldots$, where for every $i$, $a_i \in A$ is chosen ... more >>>

Eshan Chattopadhyay, Pooya Hatami, Kaave Hosseini, Shachar Lovett

We propose a new framework for constructing pseudorandom generators for $n$-variate Boolean functions. It is based on two new notions. First, we introduce fractional pseudorandom generators, which are pseudorandom distributions taking values in $[-1,1]^n$. Next, we use a fractional pseudorandom generator as steps of a random walk in $[-1,1]^n$ that ... more >>>

Sumegha Garg, Ran Raz, Avishay Tal

A line of recent works showed that for a large class of learning problems, any learning algorithm requires either super-linear memory size or a super-polynomial number of samples [Raz16,KRT17,Raz17,MM18,BOGY18,GRT18]. For example, any algorithm for learning parities of size $n$ requires either a memory of size $\Omega(n^{2})$ or an exponential number ... more >>>