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An optimization-diversification approach to portfolio selection

JOURNAL OF GLOBAL optimization 2020年第2期76卷 245-265页

作者： Cesarone, Francesco Scozzari, Andrea Tardella, Fabio Univ Roma Tre Dipartimento Econ Aziendale Rome Italy Univ Niccolo Cusano Fac Econ Rome Italy Sapienza Univ Roma Dipartimento Metodi & Modelli Econ Terr & Finanza Rome Italy

The classical approaches to optimal portfolio selection call for finding a feasible portfolio that optimizes a risk measure, or a gain measure, or a combination thereof by means of a utility function or of a performance measure. However, the optimization approach tends to amplify the estimation errors on the parameters required by the model, such as expected returns and covariances. For this reason, the Risk Parity model, a novel risk diversification approach to portfolio selection, has been recently theoretically developed and used in practice, mainly for the case of the volatility risk measure. Here we first provide new theoretical results for the Risk Parity approach for general risk measures. Then we propose a novel framework for portfolio selection that combines the diversification and the optimization approaches through the global solution of a hard nonlinear mixed integer or pseudo boolean problem. For the latter problem we propose an efficient and accurate Multi-Greedy heuristic that extends the classical single-threaded greedy approach to a multiple-threaded setting. Finally, we provide empirical results on real-world data showing that the diversified optimal portfolios are only slightly suboptimal in-sample with respect to optimal portfolios, and generally show improved out-of-sample performance with respect to their purely diversified or purely optimized counterparts.

关键词： Portfolio optimization Risk diversification Risk Parity pseudo-boolean optimization

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Virtual machine consolidation using constraint-based multi-objective optimization

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JOURNAL OF HEURISTICS 2019年第3期25卷 339-375页

作者： Terra-Neves, Miguel Lynce, Ines Manquinho, Vasco Univ Lisbon INESC ID Inst Super Tecn Rua Alves Redol 9 P-1000029 Lisbon Portugal

With the blooming of cloud computing, the demand for data centers has been rising greatly in recent years. Their energy consumption and environmental impact has become much more significant due to the continuous growth of data center supply. It is possible to reduce the amount of energy consumed by a data center by shutting down unnecessary servers and maintaining only a subset running, such that it is enough to fulfill the resource demand. With recent advances in virtualization technology, it even became possible to consolidate the workload of multiple under-utilized servers into a single server. However, too aggressive consolidation may lead to significant degradation of data center performance. Therefore, the problem of simultaneously minimizing energy consumption and performance degradation in a data center is a complex and challenging problem. In this paper, a novel multi-objective boolean optimization encoding for virtual machine consolidation is proposed and several approaches to solve it are described and compared. Moreover, this encoding is extended to consider anti-collocation constraints and the migration of virtual machines that are initially placed. This work is in part motivated by the great improvements in the performance of boolean optimization solvers, thus increasing their applicability and effectiveness for a wider spectrum of complex problems. In this case, specific techniques are applied to further boost the performance, namely search space reduction by symmetry breaking and heuristic reduction of the instance size. An extensive experimental evaluation shows the suitability of the proposed solution in comparison to the state-of-the-art approaches based on stochastic methods.

关键词： Virtual machine consolidation Multi-objective optimization pseudo-boolean optimization Live migration Cloud computing

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Particle Algorithms for optimization on Binary Spaces

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ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION 2013年第1期23卷 1–25页

作者： Schaefer, Christian CREST F-75775 Paris 16 France Univ Paris 09 F-75775 Paris 16 France

We discuss a unified approach to stochastic optimization of pseudo-boolean objective functions based on particle methods, including the cross-entropy method and simulated annealing as special cases. We point out the need for auxiliary sampling distributions, meaning parametric families on binary spaces, which are able to reproduce complex dependency structures, and illustrate their usefulness in our numerical experiments. We provide numerical evidence that particle-driven optimization algorithms based on parametric families yield superior results on strongly multimodal optimization problems while local search heuristics outperform them on easier problems.

关键词： Binary parametric families pseudo-boolean optimization sequential Monte Carlo cross-entropy method simulated annealing

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boolean lexicographic optimization: algorithms & applications

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2011年第3-4期62卷 317-343页

作者： Marques-Silva, Joao Argelich, Josep Graca, Ana Lynce, Ines Univ Tecn Lisboa INESC ID IST P-1100 Lisbon Portugal Univ Coll Dublin CSI CASL Dublin 2 Ireland Univ Lleida DIEI Lleida Spain Portuguese Catholic Univ Fac Engn Lisbon Portugal

Multi-Objective Combinatorial optimization (MOCO) problems find a wide range of practical application problems, some of which involving boolean variables and constraints. This paper develops and evaluates algorithms for solving MOCO problems, defined on boolean domains, and where the optimality criterion is lexicographic. The proposed algorithms build on existing algorithms for either Maximum Satisfiability (MaxSAT), pseudo-boolean optimization (PBO), or Integer Linear Programming (ILP). Experimental results, obtained on problem instances from haplotyping with pedigrees and software package dependencies, show that the proposed algorithms can provide significant performance gains over state of the art MaxSAT, PBO and ILP algorithms. Finally, the paper also shows that lexicographic optimization conditions are observed in the majority of the problem instances from the MaxSAT evaluations, motivating the development of dedicated algorithms that can exploit lexicographic optimization conditions in general MaxSAT problem instances.

关键词： boolean optimization Lexicographic optimization Maximum satisfiability pseudo-boolean optimization Haplotyping with pedigrees Software package dependencies

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optimization-based Test Scheduling for IEEE 1687 Multi-Power Domain Networks Using boolean Satisfiability 16

Optimization-based Test Scheduling for IEEE 1687 Multi-Power...

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16th International Conference on Design and Technology of Integrated Systems in Nanoscale Era (DTIS)

作者： Habiby, Payam Huhn, Sebastian Drechsler, Rolf Univ Bremen Bremen Germany DFKI GmbH Cyber Phys Syst D-28359 Bremen Germany

ISBN: (纸本)9781665436540

The IEEE 1687 Std. provides an efficient access methodology for embedded instruments in complex system-on-a-chip designs by introducing reconfigurable scan networks. This flexibility enables the reduction of the overall test access time, which significantly decreases the test costs compared to the conventional daisy-chaining method. However, the new access methodology strictly requires effective test schedulers that consider multi-power domains with individual constraints. This work proposes a novel test scheduler that orchestrates the boolean Satisfiability problem in conjunction with pseudo-boolean optimization techniques. The effectiveness of the proposed scheduler is proven by considering networks with over one thousand of so-called instruments forming industrial representative benchmark candidates.

关键词： boolean Satisfiability IEEE 1687 IJTAG pseudo-boolean optimization Reconfigurable Scan Networks Test Scheduling & Scheduling Algorithms

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Relaxation Strength for Multilinear optimization: McCormick Strikes Back 25th

Relaxation Strength for Multilinear Optimization: McCormick ...

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25th International Conference on Integer Programming and Combinatorial optimization (IPCO)

作者： Schutte, Emily Walte, Matthias Univ Luxembourg L-4365 Esch Sur Alzette Luxembourg Univ Twente Dept Appl Math Enschede Netherlands

ISBN: (纸本)9783031598340;9783031598357

We consider linear relaxations for multilinear optimization problems. In a recent paper, Khajavirad proved that the extended flower relaxation is at least as strong as the relaxation of any recursive McCormick linearization (Operations Research Letters 51 (2023) 146-152). In this paper we extend the result to more general linearizations, and present a simpler proof. Moreover, we complement Khajavirad's result by showing that the intersection of the relaxations of such linearizations and the extended flower relaxation are equally strong.

关键词： pseudo-boolean optimization multilinear optimization cutting planes extended formulations

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Reducing Output Response Aliasing Using boolean optimization Techniques 26

Reducing Output Response Aliasing Using Boolean Optimization...

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26th International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS)

作者： Hulle, Robert Fiser, Petr Schmidt, Jan Czech Tech Univ Fac Informat Technol Prague Czech Republic

ISBN: (纸本)9798350332773

In digital circuit testing, output response compaction can have a significant impact on fault coverage. The loss of fault coverage is caused by aliasing in the output response compaction. Classical approaches to reducing (eliminating) fault aliasing are based on modifications of the compactor design or modifying precomputed test sequence. In this paper, we propose a completely different approach based on a dedicated test pattern generation algorithm. The algorithm generates a test sequence with minimal aliasing for targeted faults. As the generated test sequence is tailored to given static and dynamic compactor structures, any response compactor can be used without a change in the design. We expand on our previous work, zero-aliasing ATPG, and incorporate pseudo-boolean optimization techniques in the process. The algorithm is evaluated using an LFSR-based MISR on a selection of benchmark circuits. A comparison with a state-of-the-art ATPG process without anti-aliasing measures is drawn.

关键词： ATPG multiple-target test generation SAT pseudo-boolean optimization fault aliasing response compaction test compaction LFSR

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Using Markov-Chain Mixing Time Estimates for the Analysis of Ant Colony optimization 11

Using Markov-Chain Mixing Time Estimates for the Analysis of...

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11th Foundations of Genetic Algorithms Workshop

作者： Sudholt, Dirk Univ Birmingham CERCIA Birmingham B15 2TT W Midlands England

ISBN: (纸本)9781450306331

The Markov chain Monte Carlo paradigm has developed powerful and elegant techniques for estimating the time until a Markov chain approaches a stationary distribution. This time is known as mixing time. We introduce the reader into mixing time estimations via coupling arguments and use the mixing of pheromone models for analyzing the expected optimization time of ant colony optimization. We demonstrate the approach for plateaus in pseudo-boolean optimization and derive upper bounds for the time until a target set is found. We also describe how mixing times can be estimated for MMAS ant systems on shortest path problems.

关键词： Ant colony optimization Markov chain Monte Carlo mixing time coupling pseudo-boolean optimization shortest path problems

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A Taxonomy of Exact Methods for Partial Max-SAT

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Journal of Computer Science & Technology 2013年第2期28卷 232-246页

作者： Mohamed El Bachir Menai Tasniem Nasser Al-Yahya Department of Computer Science College of Computer and Information Sciences King Saud University

Partial Maximum boolean Satisfiability （Partial Max-SAT or PMSAT） is an optimization variant of boolean satisfiability （SAT） problem, in which a variable assignment is required to satisfy all hard clauses and a maximum number of soft clauses in a boolean formula. PMSAT is considered as an interesting encoding domain to many reaHife problems for which a solution is acceptable even if some constraints are violated. Amongst the problems that can be formulated as such are planning and scheduling. New insights into the study of PMSAT problem have been gained since the introduction of the Max-SAT evaluations in 2006. Indeed, several PMSAT exact solvers have been developed based mainly on the Davis- Putnam-Logemann-Loveland （DPLL） procedure and Branch and Bound （B^B） algorithms. In this paper, we investigate and analyze a number of exact methods for PMSAT. We propose a taxonomy of the main exact methods within a general framework that integrates their various techniques into a unified perspective. We show its effectiveness by using it to classify PMSAT exact solvers which participated in the 2007~2011 Max-SAT evaluations, emphasizing on the most promising research directions.

关键词： Partial Max-SAT Davis-Putnam-Logenmann-Loveland branch and bound unsatisfiability pseudo-boolean optimization

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Using Linearizing Sets to Solve Multivariate Quadratic Equations in Algebraic Cryptanalysis

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IEEE ACCESS 2023年 11卷 120319-120333页

作者： Semenov, Alexander Antonov, Kirill Kochemazov, Stepan Pavlenko, Artem ITMO Univ Informat Technol & Programming Fac St Petersburg 197101 Russia MEPhI Univ Dept Cryptog & Comp Syst Secur Moscow 115409 Russia

In this paper we describe a class of cryptographic guess-and-determine attacks which is based on the notion of a linearizing set. A linearizing set-based attack is applied to a system of Multivariate Quadratic equations (MQ) over GF(2) field, which encodes how a considered cryptographic function works. By substituting into such MQ system a random (in some strict sense) assignment of variables from a linearizing set we aim to transform the system into a linear one. We introduce a probability of such an event and call it a probability of linearization. Then we describe a guess-and-determine attack, the hardness of which can be expressed via a probability of linearization. To estimate the latter it is possible to use a simple Monte Carlo algorithm. Also we describe a technique that allows to augment a considered MQ system by new linear equations and to construct a new MQ system, for which the probability of linearization is usually larger than that for an original one. For this purpose we apply a SAT oracle to a boolean formula that is naturally associated with a considered MQ system. Finally, we reduce the problem of searching for a linearizing set that yields the best effectiveness of a constructed guess-and-determine attack to a pseudo-boolean optimization problem, which can be solved using metaheuristic optimization algorithms. The important consequence of this is that this way we can construct guess-and-determine attacks automatically by solving the corresponding optimization problem. In the computational experiments we used the proposed methodology to construct attacks on several well-known stream ciphers. The runtime estimations of some of the attacks make it possible to implement them in reasonable time.

关键词： Algebraic cryptanalysis guess-and-determine attack system of multivariate quadratic equations boolean satisfiability pseudo-boolean optimization evolutionary algorithm

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