It has been about a year since I joined the Zuse Institute to work on optimization methods and computation. One of the key projects of the first half of 2021 has been on building up FrankWolfe.jl, a framework for nonlinear optimization in Julia using Frank-Wolfe methods. You can find a paper introducing the package here. This was an opportunity to experiment with different design choices for efficient, scalable, and flexible optimization tools while keeping the code simple to read and close to the algorithms.

Differentiating set projections.

The Pandora box from simple set membership.

Constrained optimization on a fundamental engineering problem

What can automated gradient computations bring to mathematical optimizers, what does it take to compute?

Compiling mathematical optimization problems in a multiple-dispatch context.

Cracking Benders decomposition, one cut at a time.

Some digging in representations for optimization modelling

Making social events easier as a graph problem.

Leveraging one of Julia central features for clearer formulation of an optimization problem.

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