Tangi Migot

Tangi Migot

Computational Scientist

Unveiling JuliaSmoothOptimizers at JuMP-dev Workshop, JuliaCon 2023

2 minute read

Published:

I am thrilled to share my experiences from the recent JuMP-dev workshop that took place at the JuliaCon 2023, held at MIT in Boston, USA. As a passionate researcher in the field of numerical optimization, this year’s conference was particularly special for me, as it marked my first in-person attendance after the previous year’s online edition (read about it here). You can check the replay of my talk on youtube.

Talk Title: Optimization solvers in JuliaSmoothOptimizers

At this year’s conference, I had the honor of presenting a talk titled “Optimization solvers in JuliaSmoothOptimizers”. In this presentation, I had the opportunity to provide an overview of the significant strides made in continuous nonlinear nonconvex optimization solvers by the JuliaSmoothOptimizers (JSO) organization. It has been an exciting journey of growth and innovation, and the conference platform was the ideal place to unveil these remarkable developments.

Abstract: Revolutionizing Optimization with JSOSuite.jl

The heart of my talk revolved around a package called JSOSuite.jl. This new addition to the JuliaSmoothOptimizers ecosystem bridges the gap between users and JSO solvers. JSOSuite.jl eliminates the need for users to be familiar with the nuances of various solvers like DCISolver.jl, FletcherPenaltySolver.jl, Percival.jl, RipQP.jl, and more. This leap in user-friendliness simplifies the optimization process and streamlines benchmarking algorithms. Notably, it paves the way for automatic algorithm selection based on specific problem characteristics.

One of the most promising features of JSOSuite.jl is its ability to facilitate benchmarking, making it easy to assess algorithm performance across different problems. This empowers researchers to make informed choices about the best optimization strategies for their particular scenarios.

Furthermore, I highlighted the performance improvements achieved by the solvers in terms of execution speed and memory utilization. This progress is a testament to the dedication and expertise of the JSO community in refining and enhancing their tools.

JuliaSmoothOptimizers: Forging the Path Forward

From my vantage point, JuliaSmoothOptimizers has solidified its position as a cutting-edge platform for studying numerical optimization and prototyping new algorithms. Over the past three years, the organization has evolved into a state-of-the-art hub for optimization enthusiasts, fostering collaboration and pushing the boundaries of what is achievable in this dynamic field.

As I reflect on my experience at JuliaCon 2023 and the incredible strides made by JuliaSmoothOptimizers, I am filled with a sense of excitement and anticipation for the future of numerical optimization. The innovations unveiled during this conference represent a significant step forward, and I am grateful to have been a part of this transformative journey.

You May Also Enjoy

Presenting at JuMP-dev 2024 and Publishing in JuliaCon 2023 Proceedings

3 minute read

Published:

I’m thrilled to share two major milestones in my recent work within the Julia ecosystem. First, I presented the latest developments in optimization solvers at JuMP-dev 2024, and second, my paper on JSOSuite.jl was accepted in The Proceedings of the JuliaCon Conferences.

These two achievements highlight both the ongoing evolution of JuliaSmoothOptimizers (JSO) and its growing impact on large-scale nonlinear optimization problems.

JuMP-dev 2024: Advancing Nonlinear Optimization with JuliaSmoothOptimizers

This year’s JuMP-dev workshop, held independently from JuliaCon for the first time in Montreal, offered a focused platform for deep dives into JuMP and its surrounding tools. In my presentation, I discussed the latest progress within the JuliaSmoothOptimizers (JSO) ecosystem, my slides and the replay.

At the core of my talk was an introduction to new solvers and packages like AdaptiveRegularization.jl, which address the unique challenges of large-scale optimization problems with Adaptive Regularization with Cubics. I emphasized the following key innovations:

  • Automatic Differentiation (AD) support and integration with JuMP for easier problem modeling.
  • Memory pre-allocation for in-place solvers, reducing runtime overhead.
  • Support for multi-precision solvers and GPU-based computations, essential for modern large-scale applications.
  • The value of factorization-free solvers, which excel in tackling large, complex problems, such as those in discretized PDE-constrained optimization.

For newcomers to JSO, JSOSuite.jl serves as a critical entry point, simplifying solver selection and benchmarking through automatic algorithm matching. This tool eliminates the complexity of choosing from multiple solvers by providing a user-friendly interface that adapts to the problem at hand. My talk also touched on the broader adoption and longevity of JSO, which now spans over 50 registered packages, making it one of the most comprehensive platforms for numerical optimization.

JuliaCon 2023: JSOSuite.jl – Simplifying Continuous Optimization

While JuMP-dev 2024 focused on recent developments, my publication in The Proceedings of the JuliaCon Conferences looks at the core philosophy and implementation behind JSOSuite.jl. Titled JSOSuite.jl: Solving Continuous Optimization Problems with JuliaSmoothOptimizers, the paper introduces JSOSuite.jl as a package designed to bring ease-of-use to complex optimization challenges.

JSOSuite.jl covers a range of problem types—from unconstrained to generally-constrained and least-squares problems—and eliminates the need for users to understand the intricate details of individual solvers. Instead, the package conducts a preliminary analysis of the problem and automatically selects the most appropriate solver, offering significant advantages to both experienced practitioners and newcomers alike.

This paper builds on the innovations within JSO, reinforcing its versatility and ease of use across various fields and applications. The package is a natural fit for researchers who need efficient, reliable solvers without the overhead of manually configuring them for different problem types.

Looking Forward

Both my presentation at JuMP-dev 2024 and the publication of the JSOSuite.jl paper reflect the significant strides made by the JuliaSmoothOptimizers organization over the past year. The JSO ecosystem is positioned to continue driving innovation in the field of numerical optimization.

I’m excited to see how these advancements will be applied across diverse optimization problems in the coming years and look forward to continuing this journey with the JSO community.

New Preprint on HAL: Exploring Projected Dynamical Systems in Geochemical Reactions

2 minute read

Published:

This project holds a special place in my heart as it touches on the very applications in geochemistry that first drew me into research. Equilibrium reactions, particularly in slow processes like the water cycle in aquifers, have always fascinated me. Moreover, this paper represents an important milestone for one of the authors, Bastien, as it was part of his Ph.D. thesis. The use of projected dynamical systems, a model I am particularly fond of, adds an additional layer of personal significance to this work.