Optimisation & control
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- Training Variational Quantum Algorithms Is NP-Hard, Even Locally
- Hamiltonian Metric Learning and Energy-Based Training: A Dissipative Geometric Framework for Optimization
- Correcting Connectivity in Arc-Based QUBO Models for Fixed-Fleet Vehicle Routing
- PL conditions do not guarantee convergence of gradient descent-ascent dynamics
- Economic resources, sporting efficiency and uncertainty: a stochastic optimization model of football performance in Africa
- Safe Position and Attitude Control for Landing on Small Celestial Bodies with Gravitational Uncertainty
- From Inverse Problems to Adjoint Gradients: A Discrete, Implementation-Oriented Tutorial on PDE-Constrained Optimization
- Scalable Distortion-Aware Clustering for Fronthaul-Limited Cell-Free MIMO Networks
- Global Optimization of Mixed-Integer Bound-Constrained Quadratic Fractional Problems
- Emergent Underactuation in Fully Actuated Multirotors by Minimizing a Homogeneous Allocation Cost
- Box-iLQR: Constraint-Aware Feedback via Finite-Barrier Trajectory Optimization
- On randomized batch-sampling coordinate descent methods for solving linear least-squares problems
- Asking the Crowd the Right Question: Bias-Cancelling Weights for Federated Learning
- Policy as Data: Replay-Based Policy Dual Averaging via Advantage Regression
- Tensor Envelopes in the Nonconvex Setting
- Gradient-Free Sampling from Generative Models via Stochastic Bounded Extremum Seeking
- Nonlinear Density-Driven Optimal Control (D2OC) for Multi-Agent Spatial Coverage via Sequential Convex Programming
- Largest Rashomon sets of decision trees for robust contextual optimization
- A KKL Observer Perspective on Reservoir Computing
- Intrinsic Path Following on SO(3)
- Amortized Score-Hamiltonian Policy Iteration: A Grid-Free Scheme for Relaxed Stochastic Control Problems
- Computing Equilibria in Integer Programming Games with Shared Constraints
- An inverse-stochastic-control Galerkin estimator for drift potentials
- Adaptive Bregman Alternating Projections for Feasible Gromov-Wasserstein Learning
- Time-reliability optimization of multi-pass underfill dispensing sequences on printed circuit boards: a schedule-dependent reliability model solved by a multiset-permutation binary-addition-tree algorithm