25 research outputs found
On Optimization Modulo Theories, MaxSMT and Sorting Networks
Optimization Modulo Theories (OMT) is an extension of SMT which allows for
finding models that optimize given objectives. (Partial weighted) MaxSMT --or
equivalently OMT with Pseudo-Boolean objective functions, OMT+PB-- is a
very-relevant strict subcase of OMT. We classify existing approaches for MaxSMT
or OMT+PB in two groups: MaxSAT-based approaches exploit the efficiency of
state-of-the-art MAXSAT solvers, but they are specific-purpose and not always
applicable; OMT-based approaches are general-purpose, but they suffer from
intrinsic inefficiencies on MaxSMT/OMT+PB problems.
We identify a major source of such inefficiencies, and we address it by
enhancing OMT by means of bidirectional sorting networks. We implemented this
idea on top of the OptiMathSAT OMT solver. We run an extensive empirical
evaluation on a variety of problems, comparing MaxSAT-based and OMT-based
techniques, with and without sorting networks, implemented on top of
OptiMathSAT and {\nu}Z. The results support the effectiveness of this idea, and
provide interesting insights about the different approaches.Comment: 17 pages, submitted at Tacas 1
Pushing the envelope of Optimization Modulo Theories with Linear-Arithmetic Cost Functions
In the last decade we have witnessed an impressive progress in the
expressiveness and efficiency of Satisfiability Modulo Theories (SMT) solving
techniques. This has brought previously-intractable problems at the reach of
state-of-the-art SMT solvers, in particular in the domain of SW and HW
verification. Many SMT-encodable problems of interest, however, require also
the capability of finding models that are optimal wrt. some cost functions. In
previous work, namely "Optimization Modulo Theory with Linear Rational Cost
Functions -- OMT(LAR U T )", we have leveraged SMT solving to handle the
minimization of cost functions on linear arithmetic over the rationals, by
means of a combination of SMT and LP minimization techniques. In this paper we
push the envelope of our OMT approach along three directions: first, we extend
it to work also with linear arithmetic on the mixed integer/rational domain, by
means of a combination of SMT, LP and ILP minimization techniques; second, we
develop a multi-objective version of OMT, so that to handle many cost functions
simultaneously; third, we develop an incremental version of OMT, so that to
exploit the incrementality of some OMT-encodable problems. An empirical
evaluation performed on OMT-encoded verification problems demonstrates the
usefulness and efficiency of these extensions.Comment: A slightly-shorter version of this paper is published at TACAS 2015
conferenc
Використання SMT–розв’язникiв для передбачення псведовипадкових послiдовностей
Робота присвячена моделюванню системи у SMT-розв’язнику Z3, яка дозволяє реалiзвати атаку на генератор
псевдовипадкових чисел, що використовується у мовi програмування Java, та атаку на реальнi веб-застосунки,
що використовують цей генератор. Проаналiзовано основнi сфери використання розв’язникiв SAT та SMT в
областi iнформацiйної безпеки. Результатом роботи є система обмежень в SMT-розв’язнику Z3, написана мовою
програмування Python, що дозволяє здiйснити атаку на методи класу java.util.Random. Результат роботи може
бути використано для тестування безпеки програмних застосункiв, зокрема таких, якi використовують генерацiю
псевдовипадкових чисел
Synthesizing Action Sequences for Modifying Model Decisions
When a model makes a consequential decision, e.g., denying someone a loan, it
needs to additionally generate actionable, realistic feedback on what the
person can do to favorably change the decision. We cast this problem through
the lens of program synthesis, in which our goal is to synthesize an optimal
(realistically cheapest or simplest) sequence of actions that if a person
executes successfully can change their classification. We present a novel and
general approach that combines search-based program synthesis and test-time
adversarial attacks to construct action sequences over a domain-specific set of
actions. We demonstrate the effectiveness of our approach on a number of deep
neural networks