PCD pins the primary gradient into the update direction and holds 70.9% accuracy at 90% sparsity on ResNet-34/CIFAR-100 while symmetric multi-objective baselines collapse
The work introduces Priority-Constrained Descent (PCD), a gradient-based framework that anchors on the primary gradient and applies only the minimal Euclidean distortion needed to guarantee each secondary objective a τ-fraction of first-order progress, reporting better per-objective performance and Pareto dominance over symmetric multi-objective methods in structured pruning, unstructured sparsity with low-rankness, and synthetic experiments, with closed-form solutions for two and three objectives and scale invariance.