Latest Research
Ideal classes of orders in quaternion algebras
We provide an algorithm that, given any order $O$ in a quaternion algebra over a global field, computes representatives of all right equivalence classes of right $O$-ideals, including the non-invertible ones.
Number Theory Rings and Algebras 11R52, 11Y40
BnB-DAQP: A Mixed-Integer QP Solver for Embedded Applications
We propose a mixed-integer quadratic programming (QP) solver that is suitable for use in embedded applications, for example, hybrid model predictive control (MPC).
Optimization and Control
Smoothing splines for discontinuous signals
But as splines are twice differentiable by construction, they cannot capture potential discontinuities in the underlying signal.
Numerical Analysis Numerical Analysis Statistics Theory Statistics Theory 65D07, 65K10, 62G08
Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization
This is the first method achieving optimal sample complexity for decentralized nonconvex stochastic composite problems, requiring $\mathcal{O}(1)$ batch size.
Optimization and Control
Knot intensity distribution: a local measure of entanglement
Rather than focusing on an exact determination, we introduce the knot intensity distribution, a local quantifier for the contribution of a curve's region to global entanglement.
Geometric Topology Soft Condensed Matter 57K10, 82D60
The Burer-Monteiro SDP method can fail even above the Barvinok-Pataki bound
When the maximum allowed rank $p$ of the SDP solution is above the Barvinok-Pataki bound (where a globally optimal solution of rank at most $p$ is guaranteed to exist), a recent line of work established convergence to a global optimum for generic or smoothed instances of the problem.
Optimization and Control Data Structures and Algorithms
A Neural Network-Based Distributional Constraint Learning Methodology for Mixed-Integer Stochastic Optimization
antonioalcantaramata/DistCL
•
•
One of the main ideas to address this trade-off is the so-called Constraint Learning (CL) methodology, where the structures of the machine learning model can be treated as a set of constraints to be embedded within the optimization problem, establishing the relationship between a direct decision variable $x$ and a response variable $y$.
Optimization and Control
Approximation in the extended functional tensor train format
Compared to existing methods based on the functional tensor train format, our approach often reduces the required storage, sometimes considerably, while achieving the same accuracy.
Numerical Analysis Numerical Analysis
Pp OPF -- Pandapower Implementation of Three-phase Optimal Power Flow Model
Challenges in the planning and operation of distribution networks caused by the integration of distributed energy resources (DERs) create the need for the development of tools that can be easily used by system operators, industry, and the research society but are also easily upgraded with new functionalities.
Optimization and Control
Quantifying the Individual Differences of Driver' Risk Perception with Just Four Interpretable Parameters
ChenChenGith/PODAR_individual_modeling_code
•
•
There will be a long time when automated vehicles are mixed with human-driven vehicles.
Numerical Analysis Numerical Analysis
