Maximizing a quadratic objective over unitriangular bases with non-negative 1+s action recovers the Kazhdan-Lusztig basis for all partitions of n≤7 and is conjectured to do so more generally, while minimization recovers Young's seminormal basis.
hub
Distributed optimization and statistical learning via the alternating direction method of multipliers
28 Pith papers cite this work. Polarity classification is still indexing.
28
Pith papers citing it
hub tools
citation-role summary
background 2
method 2
citation-polarity summary
representative citing papers
Deep Coordinator uses deep unfolding to adapt ADMM-DDP penalty parameters at runtime, delivering 6.18-9.44x faster comparable-quality trajectories in car and quadrotor fleet simulations while scaling to 8x larger systems.
Uncorrected Gaussian residual penalties in full-space sampling converge after marginalization to the graph-lifted reduced posterior multiplied by the inverse absolute determinant of the state Jacobian, requiring explicit determinant corrections for equivalence.
Predictive coding equals proximal gradient descent on MAP problems, with priors setting nonlinearities via proximal operators and yielding leaky firing-rate networks plus hierarchical MRFs.
PC-ALM uses dual ascent on an augmented Lagrangian to achieve exact backpropagation gradients via layer-local updates in linear networks and matching performance in nonlinear networks up to depth 128.
Sheaf-ADMM trains multi-agent systems by unrolling ADMM with sheaf-specified constraints, yielding improved MNIST robustness to shifts and higher Sudoku solve rates than MPNN baselines.
HyperDn is a configuration-conditioned predictor that transfers oracle supervision across denoising paradigms to achieve near-oracle hyperparameter prediction with few or zero target labels.
DUSG-Tomo-Net performs super-resolved gridless TomoSAR inversion by learning a Toeplitz-structured covariance representation from single-look nonuniform-baseline data via deep unfolding and projection enforcement.
The paper presents ModelPredictiveControl.jl, an open-source Julia toolkit for model predictive control including nonlinear, economic, and successive linearization variants, illustrated with CSTR and inverted pendulum simulations and benchmarked against MATLAB.
Augmented Lagrangian duality for MIQP is exact with finite norm penalties and polynomially bounded weights.
Bilevel Stackelberg model with KKT single-level reformulation and LDD-ADMM distributed algorithm coordinates LV communities and MV network on IEEE 33-bus and 206-bus LV feeders, achieving 1.7e-4 average deviation from exact solution.
Develops a local tangent-space rate-distortion theory and eigenspace-overlap diagnostic showing when physics-aligned compression necessarily degrades standard fidelity due to misaligned sensitivity directions.
A new geographically weighted penalized compositional regression model with pairwise fusion penalty is proposed to handle spatial heterogeneity and compositional covariates, demonstrated on U.S. income and COPD data.
Optimizing trajectory-trees in belief space improves performance in partially observable robotic planning by capturing observation-dependent contingencies, shown via PO-MPC with D-AuLa optimization and PO-LGP extending LGP.
An ADMM algorithm with consensus splitting solves the SCLS problem for Stackelberg prediction games using closed-form linear-system and sphere-projection steps.
RED is adapted to graph signals with deep unrolling for parameter estimation, yielding lower MSE than prior graph denoising methods on synthetic and real data.
A new adaptive multiparameter penalty selection method for multiconstraint and multiblock ADMM provides robustness to scale differences and initial parameter choices.
An RNN with novel sparse minimal gated units solves BPDN for TomoSAR super-resolution and achieves 10-20% higher double-scatterer detection rates than prior deep unrolling methods.
PatchCSI-T Transformer predicts multi-step CSI for adaptive underwater OFDM, improving BER and spectral efficiency on real UWA datasets.
A polynomial kernel with local support and Laplacian regularization in IMLS yields higher-fidelity meshes and textures from multi-view images than prior exponential-kernel formulations.
Reinforcement learning learns a policy that adapts control parameters of a regularized interior-point method, accelerating high-accuracy solutions for convex quadratic programs and generalizing across problem classes after lightweight training.
Overlapping Schwarz decomposition for nonlinear OCPs achieves local linear convergence with rate improving exponentially with overlap size, based on exponential decay of sensitivity for primal and dual solutions.
A hybrid MILP-NLP-complementarity decomposition solved via spatial/temporal ADMM yields up to 13x speedup on unbalanced AC power flow-constrained DES design for networks with 55 loads, with maximum 0.61% optimality gap.
Develops a distributed proportional fairness beamforming algorithm using ALM and three-block ADMM for interference management in HAPS-empowered vertical heterogeneous networks.
citing papers explorer
-
DUSG-Tomo-Net: A Deep Unfolded Neural Network for Super-Resolving Gridless Spaceborne SAR Tomography via Learned Toeplitz-Structured Covariance Representation
DUSG-Tomo-Net performs super-resolved gridless TomoSAR inversion by learning a Toeplitz-structured covariance representation from single-look nonuniform-baseline data via deep unfolding and projection enforcement.
-
Distributed Energy System Design including Unbalanced AC Power Flow for Large LV Networks with ADMM
A hybrid MILP-NLP-complementarity decomposition solved via spatial/temporal ADMM yields up to 13x speedup on unbalanced AC power flow-constrained DES design for networks with 55 loads, with maximum 0.61% optimality gap.