Xpress Mosel: Modeling and Programming Features for Optimization Projects

Heipcke, Susanne; Colombani, Yves

doi:10.1007/978-3-030-48439-2_82

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…The solution of Equation 10, ( X * ) is characterized by the Karush-Kuhn-Tucker condition as μ → 0 . We use Xpress solver ( 26 ) to obtain ( X * ) and the estimated TDOD demand matrices to the DTA model. The estimated profiled demand from the departure time model is calculated using an initial demand for the TDOD demand estimation problem.…”

Section: Methodsmentioning

confidence: 99%

Integration of Departure Time Choice Modeling and Dynamic Origin–Destination Demand Estimation in a Large-Scale Network

Shafiei

Saberi

2020

Transportation Research Record

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Time-dependent origin–destination (OD) demand estimation using link traffic data in a large-scale network is a highly underdetermined problem. As a result, providing an accurate initial solution is crucial for obtaining a more reliable estimated demand. In this paper, we discuss the necessity of having a comprehensive demand profiling model that considers the spatial differences of OD pairs and we demonstrate its application in the calibration of large-scale traffic assignment models. First, we apply a departure choice model that adds a time dimension to the OD demand flows concerning their spatial differences. The time-profiled demand is then fed into the time-dependent OD demand estimation problem for further adjustment. Results show that in addition to reducing the error between simulation outputs and the observed link counts, the estimated demand profile more accurately reflects the spatial correlation of the OD pairs in the large-scale network being studied. Results provide practical insights into deployment and calibration of simulation-based dynamic traffic assignment models.

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Section: Methodsmentioning

confidence: 99%

Integration of Departure Time Choice Modeling and Dynamic Origin–Destination Demand Estimation in a Large-Scale Network

Shafiei

Saberi

2020

Transportation Research Record

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“…Another family of programming languages is developed for mathematical modeling and optimization. Some particular instances are AIMMS [40], AMPL [41], LINGO [42], GAMS [43], Mosel [44], LPL [45], OMNI [46], PCOMP [46], and OPL [46]. In general, the modeling languages represent real-world problems via mathematical models that contain a proper set of relationships, such as equalities and logical expressions.…”

Section: Introductionmentioning

confidence: 99%

MxPL: A Programming Language for Matrix-Related Operations

Aydoğdu,

Pehlivan

2024

Symmetry

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It is important to establish solid mathematical knowledge throughout the education period. Matrices are important mathematical objects commonly used in many diverse disciplines, including mathematics, engineering, and science. Most problems encountered in such disciplines are represented by mathematical models with various types of matrices, and solved through some applications of matrix algebra. Although simple or advanced operations of matrices can be performed by using modern programming languages, it usually results in a large fragment of code with a low level of readability due to a complicated sequence of control statements. On the other hand, special-purpose languages handle these operations via library functions, presenting poor integration with other programming environments, and less programming flexibility and practice. This paper addresses the design and development of a programming language, called MxPL, which supports matrix-related mathematics with the provision of some basic structures and functions. Firstly, a grammar that is compatible with the usual notations of matrices is constructed and the parser is produced. Then, the code verifier and interpreter for MxPL programs are implemented. Some code examples are presented to illustrate the performance of several sophisticated matrix operations. The comparative analysis of MxPL is conducted with modern programming languages based on language features.

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“…Stand-alone AMLs rely on a custom lexer and parser to read the information contained in a file defining a model. Some well-known AMLs are JuMP [36], Pyomo [11], AMPL [18], GAMS [10] and Mosel [26]. JuMP [36] is an opensource embedded AML implemented in Julia [6].…”

Section: Introductionmentioning

confidence: 99%

“…The previous tools all communicate with various solvers, enabling the user to try different algorithms and solvers to find a solution to their problems. By contrast, Mosel [26] is a commercial stand-alone AML that communicates with only one solver, namely FICO Xpress [35]. It deals with MILP, convex quadratically constrained quadratic programming and second-order cone programming.…”

Section: Introductionmentioning

confidence: 99%

GBOML: a structure-exploiting optimization modelling language in Python

Miftari,

Berger,

Derval

et al. 2023

Optimization Methods and Software

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Mixed-Integer Linear Programs (MILPs) have many practical applications. Most modelling tools for MILPs fall in two broad categories. Indeed, tools such as algebraic modelling languages allow practitioners to compactly encode models using syntax close to mathematical notation but usually lack support for special structures, while other tools instead provide predefined components that can be easily assembled but modifying or adding new components is difficult. In this work, we present the inner workings of the Graph-Based Optimization Modelling Language (GBOML), an open-source modelling tool implemented in Python combining the strengths of both worlds. GBOML natively supports special structures that can be encoded by a hierarchical hypergraph, offers syntax close to mathematical notation and facilitates the modular construction and reuse of time-indexed models. We detail design choices enabling these features and show that they simplify problem encoding, lead to faster instance generation times and sometimes faster solve times. We benchmark the times taken by GBOML, JuMP, Plasmo, Pyomo and AMPL to generate instances of a structured MILP. We find that GBOML outperforms Plasmo and Pyomo, is tied with JuMP but is slower than AMPL. With parallel model generation, GBOML outperforms JuMP and closes the gap with AMPL. GBOML has the smallest memory footprint.

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Xpress Mosel: Modeling and Programming Features for Optimization Projects

Cited by 4 publications

References 6 publications

Integration of Departure Time Choice Modeling and Dynamic Origin–Destination Demand Estimation in a Large-Scale Network

Integration of Departure Time Choice Modeling and Dynamic Origin–Destination Demand Estimation in a Large-Scale Network

MxPL: A Programming Language for Matrix-Related Operations

GBOML: a structure-exploiting optimization modelling language in Python

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