3,640 research outputs found
Towards Verifying Nonlinear Integer Arithmetic
We eliminate a key roadblock to efficient verification of nonlinear integer
arithmetic using CDCL SAT solvers, by showing how to construct short resolution
proofs for many properties of the most widely used multiplier circuits. Such
short proofs were conjectured not to exist. More precisely, we give n^{O(1)}
size regular resolution proofs for arbitrary degree 2 identities on array,
diagonal, and Booth multipliers and quasipolynomial- n^{O(\log n)} size proofs
for these identities on Wallace tree multipliers.Comment: Expanded and simplified with improved result
Time-Space Tradeoffs for the Memory Game
A single-player game of Memory is played with distinct pairs of cards,
with the cards in each pair bearing identical pictures. The cards are laid
face-down. A move consists of revealing two cards, chosen adaptively. If these
cards match, i.e., they bear the same picture, they are removed from play;
otherwise, they are turned back to face down. The object of the game is to
clear all cards while minimizing the number of moves. Past works have
thoroughly studied the expected number of moves required, assuming optimal play
by a player has that has perfect memory. In this work, we study the Memory game
in a space-bounded setting.
We prove two time-space tradeoff lower bounds on algorithms (strategies for
the player) that clear all cards in moves while using at most bits of
memory. First, in a simple model where the pictures on the cards may only be
compared for equality, we prove that . This is tight:
it is easy to achieve essentially everywhere on this
tradeoff curve. Second, in a more general model that allows arbitrary
computations, we prove that . We prove this latter tradeoff
by modeling strategies as branching programs and extending a classic counting
argument of Borodin and Cook with a novel probabilistic argument. We conjecture
that the stronger tradeoff in fact holds even in
this general model
Deterministic Black-Box Identity Testing -Ordered Algebraic Branching Programs
In this paper we study algebraic branching programs (ABPs) with restrictions
on the order and the number of reads of variables in the program. Given a
permutation of variables, for a -ordered ABP (-OABP), for
any directed path from source to sink, a variable can appear at most once
on , and the order in which variables appear on must respect . An
ABP is said to be of read , if any variable appears at most times in
. Our main result pertains to the identity testing problem. Over any field
and in the black-box model, i.e. given only query access to the polynomial,
we have the following result: read -OABP computable polynomials can be
tested in \DTIME[2^{O(r\log r \cdot \log^2 n \log\log n)}].
Our next set of results investigates the computational limitations of OABPs.
It is shown that any OABP computing the determinant or permanent requires size
and read . We give a multilinear polynomial
in variables over some specifically selected field , such that
any OABP computing must read some variable at least times. We show
that the elementary symmetric polynomial of degree in variables can be
computed by a size read OABP, but not by a read OABP, for
any . Finally, we give an example of a polynomial and two
variables orders , such that can be computed by a read-once
-OABP, but where any -OABP computing must read some variable at
least $2^n
Optimization guide for programs compiled under IBM FORTRAN H (OPT=2)
Guidelines are given to provide the programmer with various techniques for optimizing programs when the FORTRAN IV H compiler is used with OPT=2. Subroutines and programs are described in the appendices along with a timing summary of all the examples given in the manual
Streaming and Query Once Space Complexity of Longest Increasing Subsequence
Longest Increasing Subsequence (LIS) is a fundamental problem in
combinatorics and computer science. Previously, there have been numerous works
on both upper bounds and lower bounds of the time complexity of computing and
approximating LIS, yet only a few on the equally important space complexity.
In this paper, we further study the space complexity of computing and
approximating LIS in various models. Specifically, we prove non-trivial space
lower bounds in the following two models: (1) the adaptive query-once model or
read-once branching programs, and (2) the streaming model where the order of
streaming is different from the natural order.
As far as we know, there are no previous works on the space complexity of LIS
in these models. Besides the bounds, our work also leaves many intriguing open
problems.Comment: This paper has been accepted to COCOON 202
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