SDP
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Related Articles from SNS
Suboptimality bounds for trace-bounded SDPs enable a faster and scalable low-rank SDP solver SDPLR+
arXiv:2406.10407v3 Announce Type: replace-cross Abstract: Semidefinite programs (SDPs) and their solvers are powerful tools with many applications in machine learning and data science. Designing scalable SDP solvers is challenging because by standard the positive semidefinite decision variable is an $n \times n$ dense matrix, even though the input is often an $n \times n$ sparse matrix. However, the solution may not require a full-rank matrix, as shown by Barvinok and Pataki.
Model Parallelism With Subnetwork Data Parallelism
Announce Type: replace Abstract: Pre-training large neural networks at scale imposes heavy memory demands on accelerators and often requires costly communication. We introduce Subnetwork Data Parallelism (SDP), a distributed training framework that partitions a model into structured subnetworks trained across workers without exchanging activations. We study two complementary masking regimes: backward masking, which applies sparsity only in the backward step to retain unbiased gradients, and...
A Perturbed q-Tsallis Self-Concordant Barrier for Spectrally Robust Semidefinite Programming
Announce Type: cross Abstract: We introduce and analyse a perturbed $q$-Tsallis barrier for semidefinite programming (SDP), defined as a spectral perturbation of the classical log-det barrier on the cone of positive definite matrices. The barrier introduces eigenvalue-adaptive stiffening through a Tsallis-type matrix-power term controlled by parameters $q>1$ and $\eta\geq0$. Our main theoretical contribution is a sharp characterisation of the differential self-concordance regime of the...
Conditioned free-energy density of proteins using unbalanced solutions to constraint satisfaction problems
arXiv:2606.01329v1 Announce Type: new Abstract: We show that computing the log-partition function (free-energy) of conditioned inhomogeneous Curie--Weiss spin Hamiltonians reduces to an unbalanced $2 \to 1$ norm computation, and design a polynomial-time SDP algorithm for this problem with a lower bound proof for the amount of unbalance achieved. Applied to the protein Ubiquitin, the framework starts from a known crystal structure, explores alternative backbone conformations across the...
Set-Supervised Diffusion Policy: Learning Action-Chunking Diffusion through Corrections
arXiv:2606.01865v1 Announce Type: new Abstract: Diffusion policies have recently emerged as a powerful framework for robotic manipulation. However, like other behavior cloning methods, they remain vulnerable to distributional shift, often requiring human-in-the-loop interventions to correct failures during deployment. These interactions naturally provide paired supervision in the form of the robot's undesired actions and the human teacher's corrective actions.
Exploiting Chordal Sparsity for Globally Optimal Estimation with Factor Graphs
arXiv:2605.30617v1 Announce Type: new Abstract: Robust and efficient state estimation is crucial for perception, navigation, and control in robotics. State estimation problems are conveniently modeled using the factor-graph framework as enabled by modern software packages such as GTSAM or g2o. However, the standard solvers included in such frameworks are local and may converge to poor local minima, posing significant safety concerns.
A Reproducible and Physically Feasible Dynamic Parameter Identification Framework for a Low-Cost Robot Arm
Announce Type: replace Abstract: This paper presents a reproducible and physically feasible dynamic parameter identification framework for CRANE-X7, a low-cost robot arm driven by modular smart actuators. To improve practical identifiability, products of inertia are removed according to approximate link symmetry, reducing the rigid-body model from 65 to 39 base parameters. Identification motions are hand-designed from structured single-joint and adjacent-joint primitives under practical...
L-SDPPO: Policy Optimization of Spiking Diffusion Policy for Intra-vehicular Robotic Manipulation
arXiv:2606.06049v1 Announce Type: new Abstract: Intra-vehicular robots in spacecraft help reduce astronaut workload and improve mission efficiency. Recent research focuses on using deep learning methods to achieve the acute control required for operations in these complex environments. However, objects exhibit unpredictable, unconstrained drift without gravitational damping.
Coherent Swap Regret and Channel-Proof Learning
arXiv:2606.02655v1 Announce Type: cross Abstract: External regret certifies stability only against replacing one's behavior by a fixed alternative. In a quantum game, this misses a natural physical move: a player can apply a local completely positive trace-preserving (CPTP) map to the state it actually received or prepared.