Data Anonymous

This research package accompanies the manuscript “Dynamic Linear Dimensionality Reduction-Based Optimizer Switching for NeuralNetworks in Robotic Sorting.” The package contains the data, source code, selected model checkpoints, frozen experimental configurations, and machine-readable results used in the five experiments reported in the manuscript. The classification dataset comprises 1,600 training images, 400 validation images, and 800 independent test images covering four simulated object classes: milk, bread, cereal, and can. FourHDF5 datasets provide simulator-state inputs and Cartesian waypoint targets for class-specific grasp prediction. The packagealso includes one selected image-classification checkpoint and four selected waypoint-prediction checkpoints. The engineering workflow is organized as image-classification training with SGD followed by trajectory-PCA-based P-BFGS continuation, and waypoint-prediction training with AdamW followed by P-BFGS continuation. Final validation jointly evaluates the classifier and the four waypoint regressors. Manuscript-specific comparison logic is separated into five experiment modules.These modules include the P-SGD baseline, multi-seed comparisons, pure-AdamW baselines, frozen P-BFGS update budgets,convergence-confirmation rules, component ablations, and the comparison between class-specific waypoint prediction and a shared geometric prior. The archive provides JSON, JSONL, CSV, NPZ, HDF5, PNG, and PyTorch checkpoint files, together with a SHA-256 file manifest and a reproducibility guide. Third-party software, including robosuite, MuJoCo, and PyTorch, is not redistributed and must be installed separately. Intermediate checkpoints generated during hyperparameter searches are excluded because of their size; therefore, the package supports result auditing, selected-model evaluation, and reconstruction of the reported tables and figures, while a complete replay of all configuration searches requires additional computation. The execution experiment uses simulator-state inputs and an assisted post-grasp backend. Its terminal-completion results should not be interpreted as autonomous physical grasp success.