51,738 research outputs found
New Results on Cutting Plane Proofs for Horn Constraint Systems
In this paper, we investigate properties of cutting plane based refutations for a class of integer programs called Horn constraint systems (HCS). Briefly, a system of linear inequalities A * x >= b is called a Horn constraint system, if each entry in A belongs to the set {0,1,-1} and furthermore there is at most one positive entry per row. Our focus is on deriving refutations i.e., proofs of unsatisfiability of such programs using cutting planes as a proof system. We also look at several properties of these refutations. Horn constraint systems can be considered as a more general form of propositional Horn formulas, i.e., CNF formulas with at most one positive literal per clause. Cutting plane calculus (CP) is a well-known calculus for deciding the unsatisfiability of propositional CNF formulas and integer programs. Usually, CP consists of a pair of inference rules. These are called the addition rule (ADD) and the division rule (DIV). In this paper, we show that cutting plane calculus is still complete for Horn constraints when every intermediate constraint is required to be Horn. We also investigate the lengths of cutting plane proofs for Horn constraint systems
Resolution over Linear Equations and Multilinear Proofs
We develop and study the complexity of propositional proof systems of varying
strength extending resolution by allowing it to operate with disjunctions of
linear equations instead of clauses. We demonstrate polynomial-size refutations
for hard tautologies like the pigeonhole principle, Tseitin graph tautologies
and the clique-coloring tautologies in these proof systems. Using the
(monotone) interpolation by a communication game technique we establish an
exponential-size lower bound on refutations in a certain, considerably strong,
fragment of resolution over linear equations, as well as a general polynomial
upper bound on (non-monotone) interpolants in this fragment.
We then apply these results to extend and improve previous results on
multilinear proofs (over fields of characteristic 0), as studied in
[RazTzameret06]. Specifically, we show the following:
1. Proofs operating with depth-3 multilinear formulas polynomially simulate a
certain, considerably strong, fragment of resolution over linear equations.
2. Proofs operating with depth-3 multilinear formulas admit polynomial-size
refutations of the pigeonhole principle and Tseitin graph tautologies. The
former improve over a previous result that established small multilinear proofs
only for the \emph{functional} pigeonhole principle. The latter are different
than previous proofs, and apply to multilinear proofs of Tseitin mod p graph
tautologies over any field of characteristic 0.
We conclude by connecting resolution over linear equations with extensions of
the cutting planes proof system.Comment: 44 page
The Structure of Differential Invariants and Differential Cut Elimination
The biggest challenge in hybrid systems verification is the handling of
differential equations. Because computable closed-form solutions only exist for
very simple differential equations, proof certificates have been proposed for
more scalable verification. Search procedures for these proof certificates are
still rather ad-hoc, though, because the problem structure is only understood
poorly. We investigate differential invariants, which define an induction
principle for differential equations and which can be checked for invariance
along a differential equation just by using their differential structure,
without having to solve them. We study the structural properties of
differential invariants. To analyze trade-offs for proof search complexity, we
identify more than a dozen relations between several classes of differential
invariants and compare their deductive power. As our main results, we analyze
the deductive power of differential cuts and the deductive power of
differential invariants with auxiliary differential variables. We refute the
differential cut elimination hypothesis and show that, unlike standard cuts,
differential cuts are fundamental proof principles that strictly increase the
deductive power. We also prove that the deductive power increases further when
adding auxiliary differential variables to the dynamics
A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints
We develop a simple routine unifying the analysis of several important
recently-developed stochastic optimization methods including SAGA, Finito, and
stochastic dual coordinate ascent (SDCA). First, we show an intrinsic
connection between stochastic optimization methods and dynamic jump systems,
and propose a general jump system model for stochastic optimization methods.
Our proposed model recovers SAGA, SDCA, Finito, and SAG as special cases. Then
we combine jump system theory with several simple quadratic inequalities to
derive sufficient conditions for convergence rate certifications of the
proposed jump system model under various assumptions (with or without
individual convexity, etc). The derived conditions are linear matrix
inequalities (LMIs) whose sizes roughly scale with the size of the training
set. We make use of the symmetry in the stochastic optimization methods and
reduce these LMIs to some equivalent small LMIs whose sizes are at most 3 by 3.
We solve these small LMIs to provide analytical proofs of new convergence rates
for SAGA, Finito and SDCA (with or without individual convexity). We also
explain why our proposed LMI fails in analyzing SAG. We reveal a key difference
between SAG and other methods, and briefly discuss how to extend our LMI
analysis for SAG. An advantage of our approach is that the proposed analysis
can be automated for a large class of stochastic methods under various
assumptions (with or without individual convexity, etc).Comment: To Appear in Proceedings of the Annual Conference on Learning Theory
(COLT) 201
Inequalities for the Ranks of Quantum States
We investigate relations between the ranks of marginals of multipartite
quantum states. These are the Schmidt ranks across all possible bipartitions
and constitute a natural quantification of multipartite entanglement
dimensionality. We show that there exist inequalities constraining the possible
distribution of ranks. This is analogous to the case of von Neumann entropy
(\alpha-R\'enyi entropy for \alpha=1), where nontrivial inequalities
constraining the distribution of entropies (such as e.g. strong subadditivity)
are known. It was also recently discovered that all other \alpha-R\'enyi
entropies for satisfy only one trivial linear
inequality (non-negativity) and the distribution of entropies for
is completely unconstrained beyond non-negativity. Our result
resolves an important open question by showing that also the case of \alpha=0
(logarithm of the rank) is restricted by nontrivial linear relations and thus
the cases of von Neumann entropy (i.e., \alpha=1) and 0-R\'enyi entropy are
exceptionally interesting measures of entanglement in the multipartite setting
Narrow Proofs May Be Maximally Long
We prove that there are 3-CNF formulas over n variables that can be refuted
in resolution in width w but require resolution proofs of size n^Omega(w). This
shows that the simple counting argument that any formula refutable in width w
must have a proof in size n^O(w) is essentially tight. Moreover, our lower
bound generalizes to polynomial calculus resolution (PCR) and Sherali-Adams,
implying that the corresponding size upper bounds in terms of degree and rank
are tight as well. Our results do not extend all the way to Lasserre, however,
where the formulas we study have proofs of constant rank and size polynomial in
both n and w
KYP Lemma for Non-Strict Inequalities and the associated Minimax Theorem
Several variations of the classical Kalman-Yakubovich-Popov Lemma, as well
the associated minimax theorem are presented.Comment: 24 page
Approximability and proof complexity
This work is concerned with the proof-complexity of certifying that
optimization problems do \emph{not} have good solutions. Specifically we
consider bounded-degree "Sum of Squares" (SOS) proofs, a powerful algebraic
proof system introduced in 1999 by Grigoriev and Vorobjov. Work of Shor,
Lasserre, and Parrilo shows that this proof system is automatizable using
semidefinite programming (SDP), meaning that any -variable degree- proof
can be found in time . Furthermore, the SDP is dual to the well-known
Lasserre SDP hierarchy, meaning that the "-round Lasserre value" of an
optimization problem is equal to the best bound provable using a degree- SOS
proof. These ideas were exploited in a recent paper by Barak et al.\ (STOC
2012) which shows that the known "hard instances" for the Unique-Games problem
are in fact solved close to optimally by a constant level of the Lasserre SDP
hierarchy.
We continue the study of the power of SOS proofs in the context of difficult
optimization problems. In particular, we show that the Balanced-Separator
integrality gap instances proposed by Devanur et al.\ can have their optimal
value certified by a degree-4 SOS proof. The key ingredient is an SOS proof of
the KKL Theorem. We also investigate the extent to which the Khot--Vishnoi
Max-Cut integrality gap instances can have their optimum value certified by an
SOS proof. We show they can be certified to within a factor .952 ()
using a constant-degree proof. These investigations also raise an interesting
mathematical question: is there a constant-degree SOS proof of the Central
Limit Theorem?Comment: 34 page
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