The authors construct a Mortensen-type observer on the Wasserstein space P2(R^d), establish dynamic programming and viscosity solution properties for the associated HJB equation using two formulations, prove uniqueness via comparison, and introduce a convergent semi-Lagrangian scheme.
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9 Pith papers cite this work. Polarity classification is still indexing.
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Presents a stochastic gradient algorithm for non-separable optimization with local convergence guarantees under smoothness assumptions.
Finite-dimensional RKHS approximation via n-widths enables scenario optimization to deliver violation guarantees on nonlinear one-step predictors without a priori bounds on the true RKHS norm or Lipschitz constant.
A robust Riemannian Levenberg-Marquardt algorithm is formulated in block-wise form, with convergence results carried over from prior work and demonstrated via an open-source Manopt.jl implementation on tasks including geodesic regression and Procrustes analysis.
Explicit formulas are proven for the depth functions of powers of cover ideals of path graphs.
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.
Derives explicit coderivative formulas for projections onto isotone cones and applies them to covering constants and Aubin property conditions for parametric complementarity problems.
Low-latency analytical and numerical quasi-static models for UAV tether aerodynamics are proposed and validated with load cell tests.
Authors apply integer programming, meta-heuristics, and algebraic techniques to generate almost orthogonal arrays that outperform prior constructions on several non-orthogonality criteria.
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The Mortensen observer on the space of probability measures
The authors construct a Mortensen-type observer on the Wasserstein space P2(R^d), establish dynamic programming and viscosity solution properties for the associated HJB equation using two formulations, prove uniqueness via comparison, and introduce a convergent semi-Lagrangian scheme.
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A stochastic gradient algorithm for non-separable optimization with convergence guarantee
Presents a stochastic gradient algorithm for non-separable optimization with local convergence guarantees under smoothness assumptions.
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Robust Nonlinear System Identification in Reproducing Kernel Hilbert Spaces via Scenario Optimization
Finite-dimensional RKHS approximation via n-widths enables scenario optimization to deliver violation guarantees on nonlinear one-step predictors without a priori bounds on the true RKHS norm or Lipschitz constant.
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A modified Riemannian Levenberg-Marquardt Algorithm for robust or constraint optimization on manifolds
A robust Riemannian Levenberg-Marquardt algorithm is formulated in block-wise form, with convergence results carried over from prior work and demonstrated via an open-source Manopt.jl implementation on tasks including geodesic regression and Procrustes analysis.
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The depth function of powers of cover ideals of path graphs
Explicit formulas are proven for the depth functions of powers of cover ideals of path graphs.
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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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Variational Analysis of Metric Projections onto Isotone Projection Cones via Coderivatives
Derives explicit coderivative formulas for projections onto isotone cones and applies them to covering constants and Aubin property conditions for parametric complementarity problems.
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Almost Orthogonal Arrays: Search Three Ways
Authors apply integer programming, meta-heuristics, and algebraic techniques to generate almost orthogonal arrays that outperform prior constructions on several non-orthogonality criteria.