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• The GPU-native trajectory optimization framework achieves a sustained 4x throughput speedup over a heavily optimized 12-core CPU baseline.result 95%
• The framework reduces energy consumption by 51% compared to a heavily optimized 12-core CPU baseline.result 95%
• The algorithm maintains over 96% active GPU utilization during operation.technical 95%
• The framework achieves planning rates exceeding 100 Hz for trajectory optimization tasks.result 95%
• The algorithm combines sequential convex programming with a consensus-based alternating direction method of multipliers.method 90%
• The framework uses a temporal splitting strategy to decouple the optimization horizon into independent, per-node subproblems that execute massively in parallel.method 90%
• The entire optimization process runs fully on the GPU, eliminating costly memory transfers and large-scale sparse factorizations.technical 90%
• The framework is extensible to robust Model Predictive Control by jointly optimizing dynamically coupled scenarios under stochastic disturbances.method 85%

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