The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Luca Trevisan - One of the best experts on this subject based on the ideXlab platform.
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pseudorandomness and average Case Complexity via uniform reductions
Computational Complexity, 2007Co-Authors: Luca Trevisan, Salil VadhanAbstract:Impagliazzo and Wigderson (1998) gave the first construction of pseudorandom generators from a uniform Complexity assumption on EXP (namely EXP ≠ BPP). Unlike results in the nonuniform setting, their result does not provide a continuous trade-off between worst-Case hardness and pseudorandomness, nor does it explicitly establish an average-Case hardness result. In this paper:
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average Case Complexity
Foundations and Trends in Theoretical Computer Science, 2006Co-Authors: Andrej Bogdanov, Luca TrevisanAbstract:We survey the average-Case Complexity of problems in NP. We discuss various notions of good-on-average algorithms, and present completeness results due to Impagliazzo and Levin. Such completeness results establish the fact that if a certain specific (but somewhat artificial) NP problem is easy-on-average with respect to the uniform distribution, then all problems in NP are easy-on-average with respect to all samplable distributions. Applying the theory to natural distributional problems remain an outstanding open question. We review some natural distributional problems whose average-Case Complexity is of particular interest and that do not yet fit into this theory. A major open question is whether the existence of hard-on-average problems in NP can be based on the P ≠ NP assumption or on related worst-Case assumptions. We review negative results showing that certain proof techniques cannot prove such a result. While the relation between worst-Case and average-Case Complexity for general NP problems remains open, there has been progress in understanding the relation between different "degrees" of average-Case Complexity. We discuss some of these "hardness amplification" results.
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average Case Complexity
Electronic Colloquium on Computational Complexity, 2006Co-Authors: Andrej Bogdanov, Luca TrevisanAbstract:We survey the average-Case Complexity of problems in NP. We discuss various notions of good-on-average algorithms, and present completeness results due to Impagliazzo and Levin. Such completeness results establish the fact that if a certain specific (but somewhat artificial) NP problem is easy-on-average with respect to the uniform distribution, then all problems in NP are easy-on-average with respect to all samplable distributions. Applying the theory to natural distributional problems remain an outstanding open question. We review some natural distributional problems whose average-Case Complexity is of particular interest and that do not yet fit into this theory. A major open question whether the existence of hard-on-average problems in NP can be based on the P$\neq$NP assumption or on related worst-Case assumptions. We review negative results showing that certain proof techniques cannot prove such a result. While the relation between worst-Case and average-Case Complexity for general NP problems remains open, there has been progress in understanding the relation between different ``degrees'' of average-Case Complexity. We discuss some of these ``hardness amplification'' results.
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pseudorandomness and average Case Complexity via uniform reductions
Conference on Computational Complexity, 2002Co-Authors: Luca Trevisan, Salil VadhanAbstract:Impagliazzo and Wigderson (1998) gave the first construction of pseudorandom generators from a uniform Complexity assumption on EXP (namely EXP = BPP). Unlike results in the nonuniform setting, their result does not provide a continuous trade-off between worst-Case hardness and pseudorandomness, nor does it explicitly establish an average-Case hardness result. We obtain an optimal worst-Case to average-Case connection for EXP: if EXP BPTIME(( )), EXP has problems that are cannot be solved on a fraction 1/2 1/'( ) of the inputs by BPTIME('( )) algorithms, for ' = /sup 1/. We exhibit a PSPACE-complete downward self-reducible and random self-reducible problem. This slightly simplifies and strengthens the proof of Impagliazzo and Wigderson (1998), which used a a P-complete problem with these properties. We argue that the results in Impagliazzo and Wigderson (1998) and in this paper cannot be proved via "black-box" uniform reductions.
Celesta A. Albonetti - One of the best experts on this subject based on the ideXlab platform.
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the avoidance of punishment a legal bureaucratic model of suspended sentences in federal white collar Cases prior to the federal sentencing guidelines
Social Forces, 1999Co-Authors: Celesta A. AlbonettiAbstract:This research proposes a legal-bureaucratic framework of sentencing in white-collar crime Cases. This framework offers a model of sentencing that goes beyond the legall extralegal debate by specifying how an interplay between legality and bureaucratic interests intervene in the relationship between defendant characteristics, Case Complexity, and sentence outcomes. In decisions to suspend punishment in federal district courts prior to the federal sentencingguidelines, findings from the structural model indicate the direct and indirect effects ofpleadingguilty and Case Complexity on the decision to suspend an incarceration sentence. In addition, thefindings offer modest supportfor the hypothesis that defendant's location in the stratification system translates into advantage at sentencing via a structural link with Case Complexity and pleadingguilty the nexus of the legal-bureaucratic model.
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Direct and Indirect Effects of Case Complexity, Guilty Pleas, and Offender Characteristics on Sentencing for Offenders Convicted of a White-Collar Offense Prior to Sentencing Guidelines
Journal of Quantitative Criminology, 1998Co-Authors: Celesta A. AlbonettiAbstract:Previous research on the punishment of offenders convicted of a white-collar offense estimated models that specify only direct effects of defendant characteristics, offense-related variables, and guilty pleas on sentence severity. Drawing from conflict or labeling theories, much of this research focused on the effects of offender's socioeconomic status on sentence outcomes. Findings from this research are inconsistent about the relationship between defendant characteristics and sentence severity. These studies overlook how differences in Case Complexity of white-collar offense and guilty pleas may intervene in the relationship between offender characteristics and sentence outcomes. This study seeks to contribute to an understanding of federal sentencing prior to the federal sentencing guidelines by testing a legal-bureaucratic theory of sentencing that hypothesizes an interplay between Case Complexity, guilty pleas and length of imprisonment. This interplay reflects the interface between the legal ramifications of pleading guilty, prosecutorial interests in efficiency and finality of Case disposition in complex white-collar Cases, and sentence severity. Using structural equation modeling, a four-equation model of sentencing that specifies Case Complexity and guilty pleas as intervening variables in the relationship between offender characteristics and length of imprisonment is estimated. Several findings are noteworthy. First, the hypothesized interplay between Case Complexity, guilty pleas, and sentence severity is supported. Second, the effect of offender's educational attainment on sentence severity is indirect via Case Complexity and guilty pleas. Third, offender's race and gender effect length of imprisonment both directly and indirectly through the intervening effect of Case Complexity and guilty pleas. These findings indicate the need to specify sentencing models that consider the direct and indirect effects of offender characteristics, offense characteristics, and guilty pleas on judicial discretion at sentencing.
Hao Yuan - One of the best experts on this subject based on the ideXlab platform.
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average Case Complexity of the min sum matrix product problem
Theoretical Computer Science, 2016Co-Authors: Ken C K Fong, Hongyu Liang, Linji Yang, Hao YuanAbstract:We study the average-Case Complexity of min-sum product of matrices, which is a fundamental operation that has many applications in computer science. We focus on optimizing the number of "algebraic" operations (i.e., operations involving real numbers) used in the computation, since such operations are usually expensive in various environments. We present an algorithm that can compute the min-sum product of two n × n real matrices using only O ( n 2 ) algebraic operations, given that the matrix elements are drawn independently and identically from some fixed probability distribution satisfying several constraints. This improves the previously best known upper-bound of O ( n 2 log ? n ) . The class of probability distributions under which our algorithm works include many important and commonly used distributions, such as uniform distributions, exponential distributions, folded normal distributions, etc.In order to evaluate the performance of the proposed algorithm, we performed experiments to compare the running time of the proposed algorithm with algorithms in 1. The experimental results demonstrate that our algorithm achieves significant performance improvement over the previous algorithms.
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average Case Complexity of the min sum matrix product problem
International Symposium on Algorithms and Computation, 2014Co-Authors: Ken C K Fong, Hongyu Liang, Linji Yang, Hao YuanAbstract:We study the average-Case Complexity of min-sum product of matrices, which is a fundamental operation that has many applications in computer science. We focus on optimizing the number of “algebraic” operations (i.e., operations involving real numbers) used in the computation, since such operations are usually expensive in various environments. We present an algorithm that can compute the min-sum product of two \(n \times n\) real matrices using only \(O(n^2)\) algebraic operations, given that the matrix elements are drawn independently and identically from some fixed probability distribution satisfying several constraints. This improves the previously best known upper-bound of \(O(n^2\log n)\). The class of probability distributions under which our algorithm works include many important and commonly used distributions, such as uniform distributions, exponential distributions, and folded normal distributions.
Sudeep Juvekar - One of the best experts on this subject based on the ideXlab platform.
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wise automated test generation for worst Case Complexity
International Conference on Software Engineering, 2009Co-Authors: Jacob Burnim, Sudeep JuvekarAbstract:Program analysis and automated test generation have primarily been used to find correctness bugs. We present Complexity testing, a novel automated test generation technique to find performance bugs. Our Complexity testing algorithm, which we call WISE (Worst-Case Inputs from Symbolic Execution), operates on a program accepting inputs of arbitrary size. For each input size, WISE attempts to construct an input which exhibits the worst-Case computational Complexity of the program. WISE uses exhaustive test generation for small input sizes and generalizes the result of executing the program on those inputs into an “input generator.” The generator is subsequently used to efficiently generate worst-Case inputs for larger input sizes. We have performed experiments to demonstrate the utility of our approach on a set of standard data structures and algorithms. Our results show that WISE can effectively generate worstCase inputs for several of these benchmarks.
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wise automated test generation for worst Case Complexity
International Conference on Software Engineering, 2009Co-Authors: Jacob Burnim, Sudeep Juvekar, Koushik SenAbstract:Program analysis and automated test generation have primarily been used to find correctness bugs. We present Complexity testing, a novel automated test generation technique to find performance bugs. Our Complexity testing algorithm, which we call WISE (Worst-Case Inputs from Symbolic Execution), operates on a program accepting inputs of arbitrary size. For each input size, WISE attempts to construct an input which exhibits the worst-Case computational Complexity of the program. WISE uses exhaustive test generation for small input sizes and generalizes the result of executing the program on those inputs into an “input generator.” The generator is subsequently used to efficiently generate worst-Case inputs for larger input sizes. We have performed experiments to demonstrate the utility of our approach on a set of standard data structures and algorithms. Our results show that WISE can effectively generate worstCase inputs for several of these benchmarks.
Jacob Burnim - One of the best experts on this subject based on the ideXlab platform.
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wise automated test generation for worst Case Complexity
International Conference on Software Engineering, 2009Co-Authors: Jacob Burnim, Sudeep JuvekarAbstract:Program analysis and automated test generation have primarily been used to find correctness bugs. We present Complexity testing, a novel automated test generation technique to find performance bugs. Our Complexity testing algorithm, which we call WISE (Worst-Case Inputs from Symbolic Execution), operates on a program accepting inputs of arbitrary size. For each input size, WISE attempts to construct an input which exhibits the worst-Case computational Complexity of the program. WISE uses exhaustive test generation for small input sizes and generalizes the result of executing the program on those inputs into an “input generator.” The generator is subsequently used to efficiently generate worst-Case inputs for larger input sizes. We have performed experiments to demonstrate the utility of our approach on a set of standard data structures and algorithms. Our results show that WISE can effectively generate worstCase inputs for several of these benchmarks.
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wise automated test generation for worst Case Complexity
International Conference on Software Engineering, 2009Co-Authors: Jacob Burnim, Sudeep Juvekar, Koushik SenAbstract:Program analysis and automated test generation have primarily been used to find correctness bugs. We present Complexity testing, a novel automated test generation technique to find performance bugs. Our Complexity testing algorithm, which we call WISE (Worst-Case Inputs from Symbolic Execution), operates on a program accepting inputs of arbitrary size. For each input size, WISE attempts to construct an input which exhibits the worst-Case computational Complexity of the program. WISE uses exhaustive test generation for small input sizes and generalizes the result of executing the program on those inputs into an “input generator.” The generator is subsequently used to efficiently generate worst-Case inputs for larger input sizes. We have performed experiments to demonstrate the utility of our approach on a set of standard data structures and algorithms. Our results show that WISE can effectively generate worstCase inputs for several of these benchmarks.