AbstractWe investigate whether circuit lower bounds for monotone circuits can be used to derandomize randomized monotone circuits. We show that, in fact, any derandomization of randomized monotone computations would derandomize all randomized computations, whether monotone or not. We prove similar results in the settings of pseudorandom generators and average-case hard functions — that a pseudorandom generator secure against monotone circuits is also secure with somewhat weaker parameters against general circuits, and that an average-case hard function for monotone circuits is also hard with somewhat weaker parameters for general circuits
We tighten the connections between circuit lower bounds and derandomization for each of the followin...
Existing proofs that deduce BPP=P from circuit lower bounds convert randomized algorithms into deter...
Existing proofs that deduce BPP=P from circuit lower bounds convert randomized algorithms into deter...
Abstract We investigate whether circuit lower bounds for monotone circuits can be used to derandomiz...
AbstractWe investigate whether circuit lower bounds for monotone circuits can be used to derandomize...
Abstract—An approximate computation of a Boolean func-tion by a circuit or switching network is a co...
An approximate computation of a function f: {0, 1}n → {0, 1} by a circuit or switching network M is ...
The study of monotonicity and negation complexity for Boolean functions has been prevalent in comple...
In several settings derandomization is known to follow from circuit lower bounds that them-selves ar...
The study of monotonicity and negation complexity for Bool-ean functions has been prevalent in compl...
Does derandomization of probabilistic algorithms become easier when the number of “bad” random input...
Does derandomization of probabilistic algorithms become easier when the number of “bad” random input...
Our main result is a combinatorial lower bounds criterion for a general model of monotone circuits, ...
Computational complexity theory and algorithms are two major areas in theoretical computer science. ...
We show that circuit lower bound proofs based on the method of random restrictions yield non-trivial...
We tighten the connections between circuit lower bounds and derandomization for each of the followin...
Existing proofs that deduce BPP=P from circuit lower bounds convert randomized algorithms into deter...
Existing proofs that deduce BPP=P from circuit lower bounds convert randomized algorithms into deter...
Abstract We investigate whether circuit lower bounds for monotone circuits can be used to derandomiz...
AbstractWe investigate whether circuit lower bounds for monotone circuits can be used to derandomize...
Abstract—An approximate computation of a Boolean func-tion by a circuit or switching network is a co...
An approximate computation of a function f: {0, 1}n → {0, 1} by a circuit or switching network M is ...
The study of monotonicity and negation complexity for Boolean functions has been prevalent in comple...
In several settings derandomization is known to follow from circuit lower bounds that them-selves ar...
The study of monotonicity and negation complexity for Bool-ean functions has been prevalent in compl...
Does derandomization of probabilistic algorithms become easier when the number of “bad” random input...
Does derandomization of probabilistic algorithms become easier when the number of “bad” random input...
Our main result is a combinatorial lower bounds criterion for a general model of monotone circuits, ...
Computational complexity theory and algorithms are two major areas in theoretical computer science. ...
We show that circuit lower bound proofs based on the method of random restrictions yield non-trivial...
We tighten the connections between circuit lower bounds and derandomization for each of the followin...
Existing proofs that deduce BPP=P from circuit lower bounds convert randomized algorithms into deter...
Existing proofs that deduce BPP=P from circuit lower bounds convert randomized algorithms into deter...