$\mathcal{L}_{2}$-Gain Tuning for the Gradient Descent Algorithm in the Presence of Disturbances.
Juan G. Rueda-EscobedoJaime A. MorenoJohannes SchifferPublished in: ECC (2022)
Keyphrases
- cost function
- dynamic programming
- worst case
- k means
- high accuracy
- computational cost
- input data
- search space
- computational complexity
- detection algorithm
- learning algorithm
- significant improvement
- theoretical analysis
- times faster
- linear programming
- neural network
- optimization algorithm
- parameter tuning
- recognition algorithm
- least squares
- preprocessing
- search algorithm
- objective function
- genetic algorithm
- markov random field
- experimental evaluation
- particle swarm optimization
- expectation maximization
- np hard
- segmentation algorithm
- classification algorithm
- matching algorithm
- optimal solution
- convergence rate
- improved algorithm