Publications associées à Moving horizon state estimation of large-scale constrained partitioned systems

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Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Michaël Unser, Martin Kurt Storath

We consider

L ^{ 1 }

-TV regularization of univariate signals with values on the real line or on the unit circle. While the real data space leads to a convex optimization problem, the problem is nonconvex for circle-valued data. In this paper, we deriv ...

SIAM2016

Moving horizon state estimation of large-scale constrained partitioned systems

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ExG Signal Feature Selection Using Hyperdimensional Computing Encoding

Logic Resynthesis of Majority-Based Circuits by Top-Down Decomposition

Iterative distributed fault detection and isolation for linear systems based on moving horizon estimation

Roundtable Panel Discussion at DAC 2019: Evolutionary Computing or Heuristic Forever?

The Complexity of Splitting Necklaces and Bisecting Ham Sandwiches

Super Resolution Phase Retrieval for Sparse Signals

Perception-aware Receding Horizon Navigation for MAVs

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

K-Adaptability in Two-Stage Distributionally Robust Binary Programming

A Comment on “Computational Complexity of Stochastic Programming Problems”

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Logic Resynthesis of Majority-Based Circuits by Top-Down Decomposition

Roundtable Panel Discussion at DAC 2019: Evolutionary Computing or Heuristic Forever?

Iterative distributed fault detection and isolation for linear systems based on moving horizon estimation

Super Resolution Phase Retrieval for Sparse Signals

The Complexity of Splitting Necklaces and Bisecting Ham Sandwiches

ExG Signal Feature Selection Using Hyperdimensional Computing Encoding

K-Adaptability in Two-Stage Distributionally Robust Binary Programming

Perception-aware Receding Horizon Navigation for MAVs

A Comment on “Computational Complexity of Stochastic Programming Problems”