scholarly journals Optlang: An algebraic modeling language for mathematical optimization

2017 ◽  
Vol 2 (9) ◽  
pp. 139 ◽  
Author(s):  
Kristian Jensen ◽  
Joao G.R. Cardoso ◽  
Nikolaus Sonnenschein
Keyword(s):  
Mathematical Optimization ◽  
Modeling Language ◽  
Algebraic Modeling Language

Optimization frameworks for machine learning: Examples and case study

2020 ◽  
Vol 62 (3-4) ◽  
pp. 169-180
Author(s):  
Joachim Giesen ◽  
Sören Laue ◽  
Matthias Mitterreiter
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Optimization Problems ◽  
Mathematical Optimization ◽  
Modeling Language ◽  
Large Problem ◽  
Validation Data ◽  
Fixed Set ◽  
New Algorithms

AbstractMathematical optimization is at the algorithmic core of machine learning. Almost any known algorithm for solving mathematical optimization problems has been applied in machine learning and the machine learning community itself is actively designing and implementing new algorithms for specific problems. These implementations have to be made available to machine learning practitioners which is mostly accomplished by distributing them as standalone software. Successful well-engineered implementations are collected in machine learning toolboxes that provide a more uniform access to the different solvers. A disadvantage of the toolbox approach is a lack of flexibility as toolboxes only provide access to a fixed set of machine learning models that cannot be modified. This can be a problem for the typical machine learning workflow that iterates the process of modeling, solving and validating. If a model does not perform well on validation data, it needs to be modified. In most cases these modifications require a new solver for the entailed optimization problems. Optimization frameworks that combine a modeling language for specifying optimization problems with a solver are better suited to the iterative workflow since they allow to address large problem classes. Here, we provide examples of the use of optimization frameworks in machine learning. We also illustrate the use of one such framework in a case study that follows the typical machine learning workflow.

SDDP.jl: A Julia Package for Stochastic Dual Dynamic Programming

2020 ◽  
Author(s):  
Oscar Dowson ◽  
Lea Kapelevich
Keyword(s):  
Dynamic Programming ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Modeling Language ◽  
Multistage Stochastic Programming ◽  
Stochastic Dual Dynamic Programming ◽  
Multiple Dispatch ◽  
Algebraic Modeling Language ◽  
User Friendly ◽  
Dual Dynamic Programming

We present SDDP.jl, an open-source library for solving multistage stochastic programming problems using the stochastic dual dynamic programming algorithm. SDDP.jl is built on JuMP, an algebraic modeling language in Julia. JuMP provides SDDP.jl with a solver-agnostic, user-friendly interface. In addition, we leverage unique features of Julia, such as multiple dispatch, to provide an extensible framework for practitioners to build on our work. SDDP.jl is well tested, and accessible documentation is available at https://github.com/odow/SDDP.jl .

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