Quantum Polak Ribière Polyak Conjugate Gradient Method
摘要
The conjugate gradient methods deflect the steepest descent method by adding to it a positive multiple of the descent direction that uses in the previous iteration (Mishra and Ram 2019b). They only require the first-order derivate and overcome the shortcomings of the slow convergence rate of the steepest descent method. The conjugate gradient methods make the gradient descent direction to conjugacy and enhance the efficiency of the algorithm.