dc-decomposition

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martindoff / DC-NN-MPC

Computationally tractable learning-based nonlinear tube MPC using difference of convex neural network dynamic approximation

deep-learning neural-network mpc convex-optimization convex robust-control mpc-control robust-mpc tube-mpc difference-of-convex-functions dc-program dc-decomposition dc-programming dc-neural-network input-convex-neural-network

Updated Aug 29, 2026
Python

martindoff / DC-RNN

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Learn dynamical systems as a difference of convex functions (DC) using a Recurrent Neural Network (RNN) architecture with DC structure. Example on the coupled tank problem.

deep-learning neural-network recurrent-neural-networks dynamical-systems difference-of-convex-function difference-of-convex-functions dc-program dc-decomposition

Updated Oct 20, 2023
Python

martindoff / DC-Deep-Neural-Network

Star

Learn dynamical systems as a difference of convex functions (DC) using a feedforward Neural Network (NN) architecture with DC structure. The resulting model learns the dynamics f in DC form as follows: f= f1 -f2 where f1 and f2 are convex functions. The DC structure of the network allows to independently express f1 and f2 as two input convex NN.

deep-learning neural-network feedforward-neural-network convex difference-of-convex-functions dc-program dc-decomposition convex-neural-network

Updated Oct 21, 2023
Python

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