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REPORTS > AUTHORS > ANDREA E. F. CLEMENTI:
All reports by Author Andrea E. F. Clementi:

TR00-054 | 5th May 2000
Andrea E. F. Clementi, Paolo Penna, Riccardo Silvestri

On the power assignment problem in radio networks

Given a finite set $S$ of points (i.e. the stations of a radio
network) on a $d$-dimensional Euclidean space and a positive integer
$1\le h \le |S|-1$, the \minrangeh{d} problem
consists of assigning transmission ranges to the stations so as
to minimize the total power consumption, provided ... more >>>


TR00-053 | 5th May 2000
Alexander E. Andreev, Andrea E. F. Clementi, Paolo Penna, Jose' D. P. Rolim

Parallel Read Operations Without Memory Contention

We address the problem of organizing a set $T$ of shared data into
the memory modules of a Distributed Memory Machine (DMM) in order
to minimize memory access conflicts (i.e. memory contention)
during read operations.
Previous solutions for this problem can be found as fundamental ... more >>>


TR97-053 | 10th November 1997
Alexander E. Andreev, J. L. Baskakov, Andrea E. F. Clementi, Jose' D. P. Rolim

Small Random Sets for Affine Spaces and Better Explicit Lower Bounds for Branching Programs

Revisions: 2

We show the following Reduction Lemma: any
$\epsilon$-biased sample space with respect to (Boolean) linear
tests is also $2\epsilon$-biased with respect to
any system of independent linear tests. Combining this result with
the previous constructions of $\epsilon$-biased sample space with
respect to linear tests, we obtain the first efficient
more >>>


TR97-011 | 7th April 1997
Alexander E. Andreev, Andrea E. F. Clementi, Jose' D.P. Rolim and Trevisan

Weak Random Sources, Hitting Sets, and BPP Simulations

We show how to simulate any BPP algorithm in polynomial time
using a weak random source of min-entropy $r^{\gamma}$
for any $\gamma >0$.
This follows from a more general result about {\em sampling\/}
with weak random sources.
Our result matches an information-theoretic lower bound ... more >>>


TR96-055 | 22nd October 1996
Alexander E. Andreev, Andrea E. F. Clementi, Jose' D. P. Rolim

Hitting Properties of Hard Boolean Operators and their Consequences on BPP

Revisions: 1 , Comments: 1

We present the first worst-case hardness conditions
on the circuit complexity of EXP functions which are
sufficient to obtain P=BPP. In particular, we show that
from such hardness conditions it is possible to construct
quick Hitting Sets Generators with logarithmic prize.
... more >>>


TR96-029 | 16th April 1996
Alexander E. Andreev, Andrea E. F. Clementi, Jose' D. P. Rolim

Towards efficient constructions of hitting sets that derandomize BPP

The efficient construction of Hitting Sets for non trivial classes
of boolean functions is a fundamental problem in the theory
of derandomization. Our paper presents a new method to efficiently
construct Hitting Sets for the class of systems of boolean linear
functions. Systems of boolean linear functions ... more >>>


TR96-016 | 6th February 1996
Andrea E. F. Clementi, Luca Trevisan

Improved Non-approximability Results for Minimum Vertex Cover with Density Constraints

We provide new non-approximability results for the restrictions
of the min-VC problem to bounded-degree, sparse and dense graphs.
We show that for a sufficiently large B, the recent 16/15 lower
bound proved by Bellare et al. extends with negligible
loss to graphs with bounded ... more >>>


TR95-061 | 27th November 1995
Alexander E. Andreev, Andrea E. F. Clementi, Jose' D. P. Rolim

Hitting sets derandomize BPP

Revisions: 1

We show that hitting sets can derandomize any BPP-algorithm.
This gives a positive answer to a fundamental open question in
probabilistic algorithms. More precisely, we present a polynomial
time deterministic algorithm which uses any given hitting set
to approximate the fractions of 1's in the ... more >>>


TR95-041 | 28th June 1995
Alexander E. Andreev, Andrea E. F. Clementi, Jose Rolim

Optimal Bounds for the Approximation of Boolean Functions and Some Applications

We prove an optimal bound on the Shannon function
$L(n,m,\epsilon)$ which describes the trade-off between the
circuit-size complexity and the degree of approximation; that is
$$L(n,m,\epsilon)\ =\
\Theta\left(\frac{m\epsilon^2}{\log(2 + m\epsilon^2)}\right)+O(n).$$
Our bound applies to any partial boolean function
and any ... more >>>




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