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.
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Distributed optimization and statistical learning via the alternating direction method of multipliers
28 Pith papers cite this work. Polarity classification is still indexing.
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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
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Kazhdan-Lusztig Basis and Optimization
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.
-
Deep-Unfolded Coordination
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.
-
Constraint residuals, graph posteriors, and determinant-corrected full-space targets in Bayesian inverse problems
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.
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Predictive Coding with Bayesian Priors via Proximal Gradients
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.
-
Augmented Lagrangian Predictive Coding
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.
-
Learning Multi-Agent Coordination via Sheaf-ADMM
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.
-
Oracle Supervision Transfers for Hyperparameter Prediction in Model-Based Image Denoising
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.
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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.
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ModelPredictiveControl.jl: advanced process control made easy in Julia
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.
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Exact Augmented Lagrangian Duality for Mixed Integer Quadratic Programming
Augmented Lagrangian duality for MIQP is exact with finite norm penalties and polynomially bounded weights.
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A Bilevel Optimization Model for Bottom-Up Coordination of Multiple Low-Voltage Energy Communities and the Medium-Voltage Network
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.
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A Geometric Lens on Physics-Aligned Data Compression
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.
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Linking COPD Prevalence with Income Distribution: A Spatial Heterogeneous Compositional Regression via Geographically Weighted Penalized Approach
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.
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Optimizing Trajectory-Trees in Belief Space: An Application from Model Predictive Control to Task and Motion Planning
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.
-
Low-Complexity Algorithm for Stackelberg Prediction Games with Global Optimality
An ADMM algorithm with consensus splitting solves the SCLS problem for Stackelberg prediction games using closed-form linear-system and sphere-projection steps.
-
Graph Signal Denoising Using Regularization by Denoising and Its Parameter Estimation
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.
-
An Adaptive Multiparameter Penalty Selection Method for Multiconstraint and Multiblock ADMM
A new adaptive multiparameter penalty selection method for multiconstraint and multiblock ADMM provides robustness to scale differences and initial parameter choices.
-
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography
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.
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Deep Learning Based Multi-Step Channel Prediction for Adaptive Underwater Acoustic OFDM Systems
PatchCSI-T Transformer predicts multi-step CSI for adaptive underwater OFDM, improving BER and spectral efficiency on real UWA datasets.
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High-Fidelity Surface Splatting-Based 3D Reconstruction from Multi-View Images
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.
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Reinforcement learning for adaptive interior point methods in convex quadratic programming
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.
-
On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control
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.
-
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.
-
Two-Level Distributed Interference Management for Large-Scale HAPS-Empowered vHetNets
Develops a distributed proportional fairness beamforming algorithm using ALM and three-block ADMM for interference management in HAPS-empowered vertical heterogeneous networks.
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Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
Introduces the Early-QaTa-COV19 dataset and reports that CSEN reaches over 97% sensitivity and over 95.5% specificity for early COVID-19 detection from X-rays.
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Faraday Tomography with the SKA: A New Era of Cosmic Magnetism Studies
A review of Faraday Rotation Measure Synthesis techniques and SKA Array Assembly stages for high-resolution Faraday tomography of cosmic magnetic structures.
- TACO: Temporal Consensus Optimization for Continual Neural Mapping
- A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling