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Parallel-in-Time Nonlinear Optimal Control via GPU-native Sequential Convex Programming

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Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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Parallel-in-Time Nonlinear Optimal Control via GPU-native Sequential Convex Programming

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
Benchmarks reveal a sustained 4x throughput speedup and a 51% reduction in energy consumption over a heavily optimized 12-core CPU baseline.
Implicationpartial
Explicitly stated in the abstract with clear numeric comparison
Verificationpartial
partial
Evidencepartial
Benchmarks reveal a sustained 4x throughput speedup and a 51% reduction in energy consumption over a heavily optimized 12-core CPU baseline.
Implicationpartial
Explicitly stated in the abstract with clear numeric evidence
Verificationpartial
partial
Evidencepartial
Crucially, the framework saturates the hardware, maintaining over 96% active GPU utilization to achieve planning rates exceeding 100 Hz.
Implicationpartial
Explicitly stated in the abstract with clear numeric evidence
Verificationpartial
partial
Evidencepartial
Crucially, the framework saturates the hardware, maintaining over 96% active GPU utilization to achieve planning rates exceeding 100 Hz.
Implicationpartial
Explicitly stated in the abstract with clear numeric evidence
Verificationpartial
partial
Evidencepartial
To bridge this gap, we introduce a fully GPU-native trajectory optimization framework that combines sequential convex programming with a consensus-based alternating direction method of multipliers.
Implicationpartial
Directly stated in the abstract as a core methodological contribution
Verificationpartial
partial
Evidencepartial
By applying a temporal splitting strategy, our algorithm decouples the optimization horizon into independent, per-node subproblems that execute massively in parallel.
Implicationpartial
Directly stated in the abstract as a key technical approach
Verificationpartial
partial
Evidencepartial
The entire process runs fully on the GPU, eliminating costly memory transfers and large-scale sparse factorizations.
Implicationpartial
Directly stated in the abstract as a key architectural feature
Verificationpartial
partial
Evidencepartial
Furthermore, we demonstrate the solver's extensibility to robust Model Predictive Control by jointly optimizing dynamically coupled scenarios under stochastic disturbances, enabling scalable and safe autonomy.
Implicationpartial
Directly stated in the abstract as a demonstrated capability
Verificationpartial
partial