ParaSCIP: A Parallel Extension of SCIP

Shinano, Yuji; Achterberg, Tobias; Berthold, Timo; Stefan, Heinz G.; Koch, Thorsten

doi:10.1007/978-3-642-24025-6_12

Cited by 36 publications

(30 citation statements)

References 7 publications

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“…Future work involves to apply our implementation to data sets with larger p and/or n. A possible choice to accomplish this involves the use of parallel computation via ParaSCIP and FiberSCIP [15]. Secondly, various non-AIC information criterion, e.g.…”

Section: Resultsmentioning

confidence: 99%

Minimization of Akaike's information criterion in linear regression analysis via mixed integer nonlinear program

Kimura

Waki

2017

Optimization Methods and Software

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Akaike's information criterion (AIC) is a measure of the quality of a statistical model for a given set of data. We can determine the best statistical model for a particular data set by the minimization of the AIC. Since we need to evaluate exponentially many candidates of the model by the minimization of the AIC, the minimization is unreasonable. Instead, stepwise methods, which are local search algorithms, are commonly used to find a better statistical model though it may not be the best.We propose a branch and bound search algorithm for a mixed integer nonlinear programming formulation of the AIC minimization by Miyashiro and Takano (2015). More concretely, we propose methods to find lower and upper bounds, and branching rules for this minimization. We then combine them with SCIP, which is a mathematical optimization software and a branch-and-bound framework. We show that the proposed method can provide the best statistical model based on AIC for small-sized or medium-sized benchmark data sets in UCI Machine Learning Repository. Furthermore, we show that this method finds good quality solutions for large-sized benchmark data sets.

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Section: Resultsmentioning

confidence: 99%

Minimization of Akaike's information criterion in linear regression analysis via mixed integer nonlinear program

Kimura

Waki

2017

Optimization Methods and Software

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“…To get a feel for the answer to that question, we performed preliminary experiments with ParaSCIP (Shinano et al 2012) employing SCIP (Achterberg 2009) as an ILP solver and using CPLEX 12 4 to solve the LP relaxations. ParaSCIP consists of a supervisor (load coordinator) system capable of maintaining the trunk of a B&B tree and distributing the solution of an ILP over a large number of PEs by use of the MPI communication protocol.…”

Section: Solution On Many Pes (Distributed Memory)mentioning

confidence: 99%

“…We primarily address algorithms that derive bounds on subproblems by solving relaxations of the original ILP that are (continuous) linear optimization problems (LPs) obtained by dropping the integrality requirements on the variables. These relaxations are typically augmented by dynamically generated valid inequalities (see, e.g., Wolsey 1998; Achterberg 2009 for details regarding general ILP solving and Xu et al 2009;Shinano et al 2012;Applegate et al 2007;Phillips et al 2006 for distributed memory solution techniques).…”

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confidence: 99%

Could we use a million cores to solve an integer program?

2012

Self Cite

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Given the steady increase in cores per CPU, it is only a matter of time before supercomputers will have a million or more cores. In this article, we investigate the opportunities and challenges that will arise when trying to utilize this vast computing power to solve a single integer linear optimization problem. We also raise the question of whether best practices in sequential solution of ILPs will be effective in massively parallel environments.

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“…Erraticism is also exploited in the context of massive parallel computing, which nowadays is becoming widely available due to multi-core technology and computer grids, allowing for the development of effective parallel MIP solvers [19,18,17]. Taking full advantage of the new architecture is far from trivial, in particular because the branching nodes produced in the earliest "ramp up" phase of the enumeration cannot be distributed in a balanced way among the processors.…”

Section: Introductionmentioning

confidence: 99%

“…Taking full advantage of the new architecture is far from trivial, in particular because the branching nodes produced in the earliest "ramp up" phase of the enumeration cannot be distributed in a balanced way among the processors. Racing ramp-up is a technique proposed in [17,16] with the aim of avoiding idle processors: a same MIP solver is initially run with different settings, in parallel, until a stopping criterion is reached. Then it is decided which of the generated trees performed best according to some criterion (not described in full details).…”

Section: Introductionmentioning

confidence: 99%

Exploiting Erraticism in Search

Fischetti

Monaci

2014

Operations Research

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High-sensitivity to initial conditions is generally viewed as a drawback of tree search methods, as it leads to an erratic behavior to be mitigated somehow. In this paper we investigate the opposite viewpoint, and consider this behavior as an opportunity to exploit. Our working hypothesis is that erraticism is in fact just a consequence of the exponential nature of tree search, that acts as a chaotic amplifier, so it is largely unavoidable. We propose a bet-and-run approach to actually turn erraticism to one's advantage. The idea is to make a number of short sample runs with randomized initial conditions, to bet on the "most promising" run selected according to certain simple criteria, and to bring it to completion. Computational results on a large testbed of mixed-integer linear programs from the literature are presented, showing the potential of this approach even when embedded in a proof-of-concept implementation.

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ParaSCIP: A Parallel Extension of SCIP

Cited by 36 publications

References 7 publications

Minimization of Akaike's information criterion in linear regression analysis via mixed integer nonlinear program

Minimization of Akaike's information criterion in linear regression analysis via mixed integer nonlinear program

Could we use a million cores to solve an integer program?

Exploiting Erraticism in Search

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