6 research outputs found
Alternation-Trading Proofs, Linear Programming, and Lower Bounds
A fertile area of recent research has demonstrated concrete polynomial time
lower bounds for solving natural hard problems on restricted computational
models. Among these problems are Satisfiability, Vertex Cover, Hamilton Path,
Mod6-SAT, Majority-of-Majority-SAT, and Tautologies, to name a few. The proofs
of these lower bounds follow a certain proof-by-contradiction strategy that we
call alternation-trading. An important open problem is to determine how
powerful such proofs can possibly be.
We propose a methodology for studying these proofs that makes them amenable
to both formal analysis and automated theorem proving. We prove that the search
for better lower bounds can often be turned into a problem of solving a large
series of linear programming instances. Implementing a small-scale theorem
prover based on this result, we extract new human-readable time lower bounds
for several problems. This framework can also be used to prove concrete
limitations on the current techniques.Comment: To appear in STACS 2010, 12 page
PosSLP and Sum of Squares
The problem PosSLP is the problem of determining whether a given
straight-line program (SLP) computes a positive integer. PosSLP was introduced
by Allender et al. to study the complexity of numerical analysis (Allender et
al., 2009). PosSLP can also be reformulated as the problem of deciding whether
the integer computed by a given SLP can be expressed as the sum of squares of
four integers, based on the well-known result by Lagrange in 1770, which
demonstrated that every natural number can be represented as the sum of four
non-negative integer squares.
In this paper, we explore several natural extensions of this problem by
investigating whether the positive integer computed by a given SLP can be
written as the sum of squares of two or three integers. We delve into the
complexity of these variations and demonstrate relations between the complexity
of the original PosSLP problem and the complexity of these related problems.
Additionally, we introduce a new intriguing problem called Div2SLP and
illustrate how Div2SLP is connected to DegSLP and the problem of whether an SLP
computes an integer expressible as the sum of three squares.
By comprehending the connections between these problems, our results offer a
deeper understanding of decision problems associated with SLPs and open avenues
for further exciting researc
Easiness Amplification and Uniform Circuit Lower Bounds
We present new consequences of the assumption that time-bounded algorithms can be "compressed" with non-uniform circuits. Our main contribution is an "easiness amplification" lemma for circuits. One instantiation of the lemma says: if n^{1+e}-time, tilde{O}(n)-space computations have n^{1+o(1)} size (non-uniform) circuits for some e > 0, then every problem solvable in polynomial time and tilde{O}(n) space has n^{1+o(1)} size (non-uniform) circuits as well. This amplification has several consequences:
* An easy problem without small LOGSPACE-uniform circuits. For all e > 0, we give a natural decision problem, General Circuit n^e-Composition, that is solvable in about n^{1+e} time, but we prove that polynomial-time and logarithmic-space preprocessing cannot produce n^{1+o(1)}-size circuits for the problem. This shows that there are problems solvable in n^{1+e} time which are not in LOGSPACE-uniform n^{1+o(1)} size, the first result of its kind. We show that our lower bound is non-relativizing, by exhibiting an oracle relative to which the result is false.
* Problems without low-depth LOGSPACE-uniform circuits. For all e > 0, 1 < d < 2, and e < d we give another natural circuit composition problem computable in tilde{O}(n^{1+e}) time, or in O((log n)^d) space (though not necessarily simultaneously) that we prove does not have SPACE[(log n)^e]-uniform circuits of tilde{O}(n) size and O((log n)^e) depth. We also show SAT does not have circuits of tilde{O}(n) size and log^{2-o(1)}(n) depth that can be constructed in log^{2-o(1)}(n) space.
* A strong circuit complexity amplification. For every e > 0, we give a natural circuit composition problem and show that if it has tilde{O}(n)-size circuits (uniform or not), then every problem solvable in 2^{O(n)} time and 2^{O(sqrt{n log n})} space (simultaneously) has 2^{O(sqrt{n log n})}-size circuits (uniform or not). We also show the same consequence holds assuming SAT has tilde{O}(n)-size circuits. As a corollary, if n^{1.1} time computations (or O(n) nondeterministic time computations) have tilde{O}(n)-size circuits, then all problems in exponential time and subexponential space (such as quantified Boolean formulas) have significantly subexponential-size circuits. This is a new connection between the relative circuit complexities of easy and hard problems
A Quantum Time-Space Lower Bound for the Counting Hierarchy
We obtain the first nontrivial time-space lower bound for quantum algorithms
solving problems related to satisfiability. Our bound applies to MajSAT and
MajMajSAT, which are complete problems for the first and second levels of the
counting hierarchy, respectively. We prove that for every real d and every
positive real epsilon there exists a real c>1 such that either: MajMajSAT does
not have a quantum algorithm with bounded two-sided error that runs in time
n^c, or MajSAT does not have a quantum algorithm with bounded two-sided error
that runs in time n^d and space n^{1-\epsilon}. In particular, MajMajSAT cannot
be solved by a quantum algorithm with bounded two-sided error running in time
n^{1+o(1)} and space n^{1-\epsilon} for any epsilon>0. The key technical
novelty is a time- and space-efficient simulation of quantum computations with
intermediate measurements by probabilistic machines with unbounded error. We
also develop a model that is particularly suitable for the study of general
quantum computations with simultaneous time and space bounds. However, our
arguments hold for any reasonable uniform model of quantum computation.Comment: 25 page
Time-Space Tradeoffs in the Counting Hierarchy
We extend the lower bound techniques of [14], to the unbounded-erro