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A tree search approach for the solution of set problems using alternative relaxations
A number of alternative relaxations for the family of set problems (FSP) in general and set covering problems (SCP) in particular are introduced and discussed. These are (i) Network flow relaxation, (ii) Assignment relaxation, (iii) Shortest route relaxation, (iv) Minimum spanning tree relaxation. A unified tree search method is developed which makes use of these relaxations. Computational experience of processing a collection of test problems is reported
A Lagrangean Relaxtion Based Algorithm for Solving Set Partitioning Problems
In this paper we discuss a solver that is developed to solve set partitioning problems.The methods used include problem reduction techniques, lagrangean relaxation and primal and dual heuristics.The optimal solution is found using a branch and bound approach.In this paper we discuss these techniques.Furthermore, we present the results of several computational experiments and compare the performance of our solver with the well-known mathematical optimization solver Cplex.algorithm;integer programming
Harvey: A Greybox Fuzzer for Smart Contracts
We present Harvey, an industrial greybox fuzzer for smart contracts, which
are programs managing accounts on a blockchain. Greybox fuzzing is a
lightweight test-generation approach that effectively detects bugs and security
vulnerabilities. However, greybox fuzzers randomly mutate program inputs to
exercise new paths; this makes it challenging to cover code that is guarded by
narrow checks, which are satisfied by no more than a few input values.
Moreover, most real-world smart contracts transition through many different
states during their lifetime, e.g., for every bid in an auction. To explore
these states and thereby detect deep vulnerabilities, a greybox fuzzer would
need to generate sequences of contract transactions, e.g., by creating bids
from multiple users, while at the same time keeping the search space and test
suite tractable. In this experience paper, we explain how Harvey alleviates
both challenges with two key fuzzing techniques and distill the main lessons
learned. First, Harvey extends standard greybox fuzzing with a method for
predicting new inputs that are more likely to cover new paths or reveal
vulnerabilities in smart contracts. Second, it fuzzes transaction sequences in
a targeted and demand-driven way. We have evaluated our approach on 27
real-world contracts. Our experiments show that the underlying techniques
significantly increase Harvey's effectiveness in achieving high coverage and
detecting vulnerabilities, in most cases orders-of-magnitude faster; they also
reveal new insights about contract code.Comment: arXiv admin note: substantial text overlap with arXiv:1807.0787
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