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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Brückner, M
19 Pith papers cite this work. Polarity classification is still indexing.
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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.
PnP-CoSMo is a modular plug-and-play iterative reconstruction technique that disentangles content and style in multi-contrast MR images to guide reconstruction from reference scans without k-space training data.
Augmented Lagrangian duality for MIQP is exact with finite norm penalties and polynomially bounded weights.
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.
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.
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.
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.
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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.
-
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.
-
A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling
PnP-CoSMo is a modular plug-and-play iterative reconstruction technique that disentangles content and style in multi-contrast MR images to guide reconstruction from reference scans without k-space training data.
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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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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.
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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.
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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.
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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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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.
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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.
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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.
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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.
- TACO: Temporal Consensus Optimization for Continual Neural Mapping