SPSA for Design of an Attentional Strategy

We utilize SPSA (Simultaneous Perturbation Stochastic Approximation) to design attentional strategies, which includes developing techniques and methodologies to capture attention, researching effective approaches for attention acquisition, and conducting experiments with data collection. By implementing SPSA algorithms, we can construct more detailed attentional frameworks and maximize their effectiveness through iterative parameter optimization. Key implementation aspects involve gradient-free optimization where the algorithm perturbs all parameters simultaneously using random vectors, making it particularly efficient for high-dimensional problems. The core SPSA update rule follows: θ_k+1 = θ_k - a_kg_k(θ_k), where g_k represents the simultaneous perturbation gradient approximation. This approach enables efficient optimization of attention strategy parameters without requiring full gradient computations, significantly reducing computational complexity while maintaining convergence properties.