pith. sign in

arxiv: 2606.20410 · v1 · pith:YHH5DP4Bnew · submitted 2026-06-18 · 💻 cs.MS

MaRDI Open Interfaces for Interoperable Nonlinear Optimization

Dmitry I. Kabanov , Stephan Rave , Mario Ohlberger This is my paper

Pith reviewed 2026-06-26 14:46 UTC · model grok-4.3

classification 💻 cs.MS
keywords nonlinear optimizationunified interfacesinteroperabilitydata marshallingscientific computingsolver switchingsoftware package
0
0 comments X

The pith

The MaRDI Open Interfaces package supplies unified APIs for nonlinear optimization and automates data marshalling across languages.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a software package whose main purpose is to reduce friction when computational scientists switch between different solvers for the same numerical task. It does this by defining common interfaces that hide solver-specific details and by handling the conversion of input and output data between languages automatically. The authors argue that these two features together let users run experiments on multiple solvers with far less custom integration code and validation work. A sympathetic reader would care because choosing or comparing solvers is a frequent step in scientific workflows, and each new solver typically demands its own wrapper and data-handling layer. The manuscript describes the package structure and illustrates the approach with concrete nonlinear-optimization examples.

Core claim

The MaRDI Open Interfaces package provides unified interfaces for typical numerical problems such as nonlinear optimization, allowing users to switch between solvers for the same problem type, and it automates data marshalling between programming languages; consequently computational scientists can conduct experiments faster with fewer code-modification and testing efforts. The work presents the general structure of the package and demonstrates it through examples that use the nonlinear-optimization interface.

What carries the argument

Unified interfaces for numerical problems together with automated data marshalling between programming languages.

If this is right

  • A user can replace one nonlinear-optimization solver with another by altering only configuration data rather than rewriting integration logic.
  • Data exchange between languages occurs without manual translation routines for each new solver-language pair.
  • Direct comparison of several solvers on identical problems becomes feasible with reduced setup overhead.
  • New solver implementations can reuse the common interface definitions instead of building language-specific bindings from scratch.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same interface pattern could be applied to other problem classes such as linear systems or time-stepping schemes to extend the interoperability benefit.
  • If multiple solver projects adopt the interfaces, a shared problem-description format might emerge without central coordination.
  • Lowered switching cost could increase the frequency with which researchers verify that performance claims hold across different language environments.

Load-bearing premise

That the unified interfaces and automated marshalling will in practice reduce the amount of code modification and testing needed when users switch solvers.

What would settle it

A controlled user study that measures lines of code changed and hours spent when the same optimization task is solved with and without the package while switching between two or more solvers.

Figures

Figures reproduced from arXiv: 2606.20410 by Dmitry I. Kabanov, Mario Ohlberger, Stephan Rave.

Figure 1
Figure 1. Figure 1: Component diagram of MaRDI Open Interfaces. libraries. Third, all communication and data passing happens in-process, that allows avoiding performance penalties, as data are passed by pointers, which is especially important for arrays as their memory size is defined by the problem at hand, and copying such data structures would be costly. The software package consists of components of different types, which… view at source ↗
read the original abstract

MaRDI Open Interfaces is a software package that aims to improve interoperability in scientific computing, particularly, for nonlinear optimization. To this end, this package holds two main characteristics. First, it provides unified interfaces for typical numerical problems to help switching between solvers for the same problem type. Second, it automates data marshalling between programming languages. Hence, computational scientists can conduct experiments faster by using the package, with fewer code-modification and testing efforts. In this work we describe the general structure of the software package and show examples with the interface for nonlinear optimization.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper describes MaRDI Open Interfaces, a software package for nonlinear optimization that supplies unified interfaces across solvers and automates data marshalling between programming languages. The stated goal is to enable computational scientists to switch solvers and conduct experiments with reduced code-modification and testing effort. The manuscript outlines the package structure and supplies interface examples but contains no benchmarks, usage metrics, or controlled comparisons.

Significance. If the interfaces demonstrably reduce effort relative to direct solver calls or existing wrappers, the work could meaningfully improve interoperability and reproducibility in mathematical software. The absence of any quantitative validation, however, leaves the practical impact unestablished.

major comments (2)
  1. [Abstract] Abstract: the central claim that the package 'hence' lets users 'conduct experiments faster ... with fewer code-modification and testing efforts' is asserted without any supporting data (lines-of-code deltas, timing measurements for solver switches, or user-task comparisons). This claim is load-bearing for the paper's motivation yet receives no empirical grounding in the described structure or examples.
  2. [Description of package structure] The manuscript supplies interface examples but no verification that the unified interfaces preserve numerical correctness or that marshalling works across the claimed language boundaries without introducing overhead or errors. Section describing the general structure should include at least basic correctness or interoperability tests.
minor comments (2)
  1. [Abstract] The title and abstract refer to 'nonlinear optimization' while the scope statement is broader; clarify the exact problem classes covered by the unified interfaces.
  2. No references are supplied to prior interoperability frameworks (e.g., existing solver wrappers or language-binding projects); adding a short related-work paragraph would help situate the contribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We respond point by point to the major comments, indicating where revisions to the manuscript are planned.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the package 'hence' lets users 'conduct experiments faster ... with fewer code-modification and testing efforts' is asserted without any supporting data (lines-of-code deltas, timing measurements for solver switches, or user-task comparisons). This claim is load-bearing for the paper's motivation yet receives no empirical grounding in the described structure or examples.

    Authors: We agree that the abstract asserts a benefit without empirical support. The manuscript is a descriptive account of the package structure and examples; it does not contain benchmarks or user studies. We will revise the abstract to state that the interfaces are designed to reduce code-modification effort through unification and automated marshalling, removing any implication of measured improvements in speed or effort. revision: yes

  2. Referee: [Description of package structure] The manuscript supplies interface examples but no verification that the unified interfaces preserve numerical correctness or that marshalling works across the claimed language boundaries without introducing overhead or errors. Section describing the general structure should include at least basic correctness or interoperability tests.

    Authors: The manuscript provides usage examples but does not include dedicated verification tests. We will add a short subsection to the structure description that reports basic correctness checks on standard nonlinear problems and confirms successful marshalling for the supported language pairs. Comprehensive overhead measurements or exhaustive cross-language validation remain outside the scope of this work. revision: partial

Circularity Check

0 steps flagged

No circularity: software description paper with no derivation chain

full rationale

The manuscript is a descriptive account of a software package providing unified interfaces and data marshalling for nonlinear optimization solvers. It contains no equations, fitted parameters, predictions, uniqueness theorems, or ansatzes. The central claim that the package reduces code-modification and testing efforts is an assertion about intended utility rather than a derived result that reduces to its own inputs by construction. No self-citation load-bearing steps or renamings of known results appear. The paper is self-contained as a software description and exhibits no circular reasoning.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper describes a software package rather than a mathematical derivation or model, so no free parameters, axioms, or invented entities are involved.

pith-pipeline@v0.9.1-grok · 5617 in / 862 out tokens · 16388 ms · 2026-06-26T14:46:17.583138+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

19 extracted references · 13 canonical work pages

  1. [1]

    Die mathematische Forschungsdateninitiative in der NFDI: MaRDI (Mathematical Research Data Initiative)

    P. Benner et al. “Die mathematische Forschungsdateninitiative in der NFDI: MaRDI (Mathematical Research Data Initiative)”. In: GAMM Rundbrief 1 (2022), pp. 40–43. doi: 21.11116/0000-000A-BC70-4

    2022

  2. [2]

    The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations

    C. G. Broyden. “The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations”. In: IMA Journal of Applied Mathematics 6.1 (1970), pp. 76–90. doi: 10.1093/imamat/6.1.76

    work page doi:10.1093/imamat/6.1.76 1970

  3. [3]

    The Convergence of a Class of Double-rank Minimization Algorithms 2. The New Algorithm

    C. G. Broyden. “The Convergence of a Class of Double-rank Minimization Algorithms 2. The New Algorithm”. In: IMA Journal of Applied Mathematics 6.3 (1970), pp. 222–231. doi: 10.1093/imamat/6.3.222

    work page doi:10.1093/imamat/6.3.222 1970

  4. [4]

    preCICE v2: A sustainable and user-friendly coupling library

    G. Chourdakis et al. “preCICE v2: A sustainable and user-friendly coupling library”. In: Open Research Europe 2 (2022), p. 51. doi: 10.12688/openreseurope. 14445.2

    work page doi:10.12688/openreseurope 2022

  5. [5]

    Maintaining Correctness in Scientific Programs

    P. F. Dubois. “Maintaining Correctness in Scientific Programs”. In: Computing in Science & Engineering 7.3 (2005), pp. 80–85. doi: 10.1109/MCSE.2005.54. 11

    work page doi:10.1109/mcse.2005.54 2005

  6. [6]

    A new approach to variable metric algorithms

    R. Fletcher. “A new approach to variable metric algorithms”. In: The Computer Journal 13.3 (1970), pp. 317–322. doi: 10.1093/comjnl/13.3.317

    work page doi:10.1093/comjnl/13.3.317 1970

  7. [7]

    A Concise Guide To Good Practices For Automated Testing And Documentation Of Research Software

    J. Fritz, M. Mesiti, and J. P. Thiele. “A Concise Guide To Good Practices For Automated Testing And Documentation Of Research Software”. In: Electronic Communications of the EASST 83 (2025). doi: 10.14279/eceasst.v83.2622

    work page doi:10.14279/eceasst.v83.2622 2025

  8. [8]

    Gamma et al

    E. Gamma et al. Design patterns: elements of reusable object-oriented software . Addison-Wesley Longman Publishing Co., 1995

    1995

  9. [9]

    Computational Optimization and Applications , volume=

    F. Gao and L. Han. “Implementing the Nelder-Mead simplex algorithm with adap- tive parameters”. In: Computational Optimization and Applications 51.1 (2012), pp. 259–277. doi: 10.1007/s10589-010-9329-3

    work page doi:10.1007/s10589-010-9329-3 2012

  10. [10]

    A family of variable-metric methods derived by variational means

    D. Goldfarb. “A family of variable-metric methods derived by variational means”. In: Mathematics of Computation 24.109 (1970), pp. 23–26. doi: 10.1090/s0025- 5718-1970-0258249-6

    work page doi:10.1090/s0025- 1970

  11. [11]

    Improving Interoperability in Sci- entific Computing via MaRDI Open Interfaces

    D. I. Kabanov, S. Rave, and M. Ohlberger. “Improving Interoperability in Sci- entific Computing via MaRDI Open Interfaces”. In: Journal of Open Research Software 13 (2025). doi: 10.5334/jors.569

    work page doi:10.5334/jors.569 2025

  12. [12]

    Optim: A mathematical optimization package for Julia

    P. K. Mogensen and A. N. Riseth. “Optim: A mathematical optimization package for Julia”. In: Journal of Open Source Software 3.24 (2018), p. 615. doi: 10.21105/ joss.00615

    2018

  13. [13]

    A Simplex Method for Function Minimization

    J. A. Nelder and R. Mead. “A Simplex Method for Function Minimization”. In: The Computer Journal 7.4 (1965), pp. 308–313. doi: 10.1093/comjnl/7.4.308

    work page doi:10.1093/comjnl/7.4.308 1965

  14. [14]

    Nocedal and S

    J. Nocedal and S. J. Wright. Numerical Optimization. 2nd ed. Springer New York,

  15. [15]

    doi: 10.1007/978-0-387-40065-5

    work page doi:10.1007/978-0-387-40065-5

  16. [16]

    Enhancing Scientific Reproducibility: A Continuous Integration Workflow for High-Performance Computing

    S. Peters et al. “Enhancing Scientific Reproducibility: A Continuous Integration Workflow for High-Performance Computing”. In: Electronic Communications of the EASST 83 (Selected Contributions of the 4 th Conference for Research Software Engineering in Germany 2025). doi: 10.14279/eceasst.v83.2625

    work page doi:10.14279/eceasst.v83.2625 2025

  17. [17]

    SciPy 1.0: Fundamental Algorithms for Scientific Com- puting In Python

    SciPy Contributors. “SciPy 1.0: Fundamental Algorithms for Scientific Com- puting In Python”. In: Nature Methods 17.3 (2020), pp. 261–272. doi: 10.1038/ s41592-019-0686-2

    2020

  18. [18]

    The Journal of Open Source Software2(11) (2017) https://doi.org/10.21105/joss

    L. Seelinger et al. “UM-Bridge: Uncertainty quantification and modeling bridge”. In: Journal of Open Source Software 8.83 (2023), p. 4748. doi: 10.21105/joss. 04748

    work page doi:10.21105/joss 2023

  19. [19]

    Conditioning of quasi-Newton methods for function minimiza- tion

    D. F. Shanno. “Conditioning of quasi-Newton methods for function minimiza- tion”. In: Mathematics of Computation 24.111 (1970), pp. 647–656. doi: 10.1090/ s0025-5718-1970-0274029-x . 12

    1970