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Found 4 papers, 2 papers with code
Integrating prediction in mean-variance portfolio optimization
Prediction models are traditionally optimized independently from their use in the asset allocation decision-making process.
Efficient differentiable quadratic programming layers: an ADMM approach
Recent advances in neural-network architecture allow for seamless integration of convex optimization problems as differentiable layers in an end-to-end trainable neural network.
Gradient boosting for convex cone predict and optimize problems
Prediction models are typically optimized independently from decision optimization.
SCQPTH: an efficient differentiable splitting method for convex quadratic programming
We present SCQPTH: a differentiable first-order splitting method for convex quadratic programs.
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