Efficient large-scale configuration via integer linear programming

2013 ◽  
Vol 27 (1) ◽  
pp. 37-49 ◽  
Author(s):  
Ingo Feinerer
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Large Scale ◽  
Unified Modeling Language ◽  
General Purpose ◽  
Linear Program ◽  
Integer Linear Program ◽  
Practical Applications ◽  
Unified Modeling ◽  
Modeling Environment

AbstractConfiguration of large-scale applications in an engineering context requires a modeling environment that allows the design engineer to draft the configuration problem in a natural way and efficient methods that can process the modeled setting and scale with the number of components. Existing configuration methods in artificial intelligence typically perform quite well in certain subareas but are hard to use for general-purpose modeling without mathematical or logics background (the so-called knowledge acquisition bottleneck) and/or have scalability issues. As a remedy to this important issue both in theory and in practical applications, we use a standard modeling environment like the Unified Modeling Language that has been proposed by the configuration community as a suitable object-oriented formalism for configuration problems. We provide a translation of key concepts of class diagrams to inequalities and identify relevant configuration aspects and show how they are treated as an integer linear program. Solving an integer linear program can be done efficiently, and integer linear programming scales well to large configurations consisting of several thousands components and interactions. We conduct an empirical study in the context of package management for operating systems and for the Linux kernel configuration. We evaluate our methodology by a benchmark and obtain convincing results in support for using integer linear programming for configuration applications of realistic size and complexity.

scholarly journals Association rule hiding using integer linear programming

2021 ◽  
Vol 11 (4) ◽  
pp. 3451
Author(s):  
Suma B. ◽  
Shobha G.
Keyword(s):  
Data Mining ◽  
Linear Programming ◽  
Integer Linear Programming ◽  
Association Rule ◽  
Real Life ◽  
Linear Program ◽  
Integer Linear Program ◽  
Optimization Approach ◽  
Integer Linear Program Formulation ◽  
The Impact

<span>Privacy preserving data mining has become the focus of attention of government statistical agencies and database security research community who are concerned with preventing privacy disclosure during data mining. Repositories of large datasets include sensitive rules that need to be concealed from unauthorized access. Hence, association rule hiding emerged as one of the powerful techniques for hiding sensitive knowledge that exists in data before it is published. In this paper, we present a constraint-based optimization approach for hiding a set of sensitive association rules, using a well-structured integer linear program formulation. The proposed approach reduces the database sanitization problem to an instance of the integer linear programming problem. The solution of the integer linear program determines the transactions that need to be sanitized in order to conceal the sensitive rules while minimizing the impact of sanitization on the non-sensitive rules. We also present a heuristic sanitization algorithm that performs hiding by reducing the support or the confidence of the sensitive rules. The results of the experimental evaluation of the proposed approach on real-life datasets indicate the promising performance of the approach in terms of side effects on the original database.</span>

Unified Modeling Language

2005 ◽  
pp. 2921-2928 ◽  
Author(s):  
Peter Fettke
Keyword(s):  
Systems Engineering ◽  
Subject Matter ◽  
Unified Modeling Language ◽  
Object Oriented ◽  
General Purpose ◽  
Modeling Language ◽  
Unified Modeling ◽  
Object Oriented Systems

Mature engineering disciplines are generally characterized by accepted methodical standards for describing all relevant artifacts of their subject matter. Such standards not only enable practitioners to collaborate, but they also contribute to the development of the whole discipline. In 1994, Grady Booch, Jim Rumbaugh, and Ivar Jacobson joined together to unify the plethora of existing object-oriented systems engineering approaches at semantic and notation level (Booch, 2002; Fowler, 2004; Rumbaugh, Jacobson, & Booch, 1998). Their effort led to the Unified Modeling Language (UML), a well-known, general-purpose, tool-supported, process-independent, and industry-standardized modeling language for visualizing, describing, specifying, and documenting systems artifacts. Table 1 depicts the origin and descent of UML.

Generic Framework for Defining Domain-Specific Models

2005 ◽  
pp. 1266-1273
Author(s):  
Arnor Solberg ◽  
John Oldevik ◽  
Audun Jensvoll
Keyword(s):  
System Development ◽  
Unified Modeling Language ◽  
General Purpose ◽  
Unified Modeling ◽  
Domain Specific ◽  
Development Teams ◽  
Software Development Teams ◽  
System Development Methodologies ◽  
State Of Affairs ◽  
Specific Modeling

As a result of the widespread popularity of the Unified Modeling Language (UML) (OMG, 2003-1), many companies have invested in introducing a UML-based methodology. There are many general purpose UML-based methodologies on the market today; among the most popular are UP (Jacobson, Booch & Rumbaugh, 1999), RUP (Kruchten, 2000), Catalysis (D’Souza & Wills, 1998), Select Perspective (Allen & Frost, 1998), and KOBRA (Atkinson et al., 2001). Typically, these general purpose software system development methodologies do not immediately fulfill a company’s need. Aiming to provide methodologies that may be applied in many domains and for many purposes, these general purpose methodologies typically become extensive and are perceived as overwhelming. At the same time they typically lack support for the more exclusive needs that the companies and domains encounter. Thereby, introducing a general purpose methodology in an organization commonly implies two particular challenges that at first sight seems to be contradictory. On one hand there is a problem that the general purpose methodology provides/prescribes far too much and encounters too many situations. On the other hand the general purpose methodology does not support specific modeling concepts, mechanisms, and techniques wanted by the particular company or development group. Thus, in that respect the general purpose methodology actually covers too little. This state of affairs is why lots of consultants, researchers, and others are in the business of helping companies to introduce these methodologies, as well as customizing general purpose methodologies to be appropriate for the actual company and purpose. The customization is typically tuned based on different criteria such as domain, kind of customers, quality demands, size of the company, and size of the software development teams. A common way of customizing a general purpose methodology is by removing, adding, and/or merging prescribed tasks, phases, roles, and models/artifacts of the methodology. However, even if introduction of a general purpose methodology almost always requires a customization effort, there does not seem to be any standard and formalized way of doing it.

scholarly journals Large-Scale Integer Linear Programming for Orientation Preserving 3D Shape Matching

2011 ◽  
Vol 30 (5) ◽  
pp. 1471-1480 ◽  
Author(s):  
Thomas Windheuser ◽  
Ulrich Schlickwei ◽  
Frank R. Schimdt ◽  
Daniel Cremers
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Large Scale ◽  
Shape Matching ◽  
3D Shape ◽  
3D Shape Matching

scholarly journals Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems

2020 ◽  
Author(s):  
Álinson S. Xavier ◽  
Feng Qiu ◽  
Shabbir Ahmed
Keyword(s):  
Machine Learning ◽  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Large Scale ◽  
Unit Commitment ◽  
Energy Sector ◽  
Mixed Integer ◽  
Data Set ◽  
Computational Performance

Security-constrained unit commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via mixed-integer linear programming (MIP), sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions, and affine subspaces where the optimal solution is likely to lie, leading to a significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that using the proposed techniques, SCUC can be solved on average 4.3 times faster with optimality guarantees and 10.2 times faster without optimality guarantees, with no observed reduction in solution quality. Out-of-distribution experiments provide evidence that the method is somewhat robust against data-set shift. Summary of Contribution. The paper describes a novel computational method, based on a combination of mixed-integer linear programming (MILP) and machine learning (ML), to solve a challenging and fundamental optimization problem in the energy sector. The method advances the state-of-the-art, not only for this particular problem, but also, more generally, in solving discrete optimization problems via ML. We expect that the techniques presented can be readily used by practitioners in the energy sector and adapted, by researchers in other fields, to other challenging operations research problems that are solved routinely.

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