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Optimization Research

Research at the intersection of big data, optimization, and explainability

Typal Academy's research efforts focus on open-source development of optimization-based tools. Our specialty is in creating optimization models and algorithms that are tunable, enabling high performance on a classes of applications where a distribution of training data is available.

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Learning to Optimize

Explainable AI via Learning to Optimize

Faster Predict-and-Optimize with Davis-Yin Splitting

Safeguarded Learned Convex Optimization

Jacobian-Free Backprop

Learn to Predict EQ via Fixed Point Networks

Feasibility-based Fixed Point Networks

Zero-Order Optimization

A Hamilton-Jacobi-based Proximal Operator

Global Solutions to Nonconvex Problems by Evolution of HJ PDEs