jaxipm is the first GPU-batched IPOPT solver in JAX using heterogeneous iteration fusion and iteration-level batching, delivering up to 32.85x higher throughput than standard IPOPT on quadrotor NMPC benchmarks.
Mpax: Mathematical programming in jax,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Derives linear sample complexity for PDHG parameters and polynomial sample complexity for full PDLP hyperparameters using data-driven algorithm design.
citing papers explorer
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Scaling Nonlinear Optimization: Many Problems One GPU
jaxipm is the first GPU-batched IPOPT solver in JAX using heterogeneous iteration fusion and iteration-level batching, delivering up to 32.85x higher throughput than standard IPOPT on quadrotor NMPC benchmarks.
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Parameter Tuning with Generalization Guarantees for GPU-Accelerated Linear Programming
Derives linear sample complexity for PDHG parameters and polynomial sample complexity for full PDLP hyperparameters using data-driven algorithm design.