A New Computationally Simple Approach for Implementing Neural Networks with Output Hard Constraints

An automatic method for finding the greatest or least value of a function

H.H. Rosenbrock · 1960

Earlier work this paper cites.

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Earlier work this paper cites.

Optnet: Differentiable optimization as a layer in neural networks

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Earlier work this paper cites.

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Jay Yoon Lee, S.V. Mehta, M. Wick, J.-B. Tristan, and J. Carbonell · 2019

Earlier work this paper cites.

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A. Agrawal, B. Amos, S. Barratt, S. Boyd, S. Diamond, and J.Z. Kolter · 2019

Earlier work this paper cites.

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M. Raissi, P. Perdikaris, and G.E. Karniadakis · 2019

Earlier work this paper cites.

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Iterative supervised learning for regression with constraints

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