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Miguel Vaquero

Publications and source records attributed to Miguel Vaquero.

16 recordsLinked to original sources
arXiv2026

A Robust Chance Constrained Approach to Surgery Scheduling

We study elective surgery scheduling under uncertain procedure durations. Schedules based on mean durations or fixed buffering rules may appear efficient ex ante but become fragile in execution, as early overruns propagate through the day and expose later surgeries to accumulated delay. We propose a robust chance-constrained framework that separates uncertainty quantification from schedule optimization. A buffer engine converts distributional information into reliability-dependent buffered durations, while the scheduling model jointly selects assignments, sequences, start times, and surgery-level reliability levels from a discrete menu. Reliability therefore becomes an endogenous scheduling decision rather than a fixed service-level parameter. The framework accommodates average-reliability, worst-day, and hard-target risk postures. Comparisons with common-reliability and uniform proportional-buffer benchmarks show that the menu derives its value from exploiting surgery-level heterogeneity, allocating protection where it has the greatest operational value while avoiding unnecessary conservatism. In a rolling-origin case study at HLA Moncloa Hospital in Madrid, covering 10 instances with 45 to 227 surgeries, the approach reduces delays exceeding 90 minutes by 97%, lowers the 95th-percentile delay by approximately 620 minutes, and reduces total overtime by 42% relative to a deterministic mean-based baseline. By allocating buffers according to uncertainty and operational exposure, the framework translates heterogeneous duration data and risk preferences into schedules that are more reliable in execution and less conservatively buffered than one-size-fits-all rules.

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arXiv2026

A Unified Discrete Gradient-SAV Framework for Structure-Preserving Integration

We present a framework combining discrete gradient (DG) methods with the Scalar Auxiliary Variable (SAV) approach to construct structure-preserving integrators for dissipative and conservative systems. The key observation is that SAV quadratization lifts the dynamics to an extended state space on which the modified energy has an exact discrete-gradient identity. This viewpoint yields three integrators with different accuracy and cost profiles: a first-order semi-implicit Forward Euler scheme, a second-order self-adjoint Midpoint scheme, and a second-order Predictive scheme with reduced implicitness. The construction extends to almost-Poisson systems and preserves selected Casimir invariants under an enforceable discrete condition. Numerical experiments cover the Allen--Cahn equation, an Ohta--Kawasaki-type nonlocal gradient flow, a double-well Hamiltonian oscillator, and a Poisson system with a nonlinear cubic Casimir.

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arXiv2024

k-Dimensional Agreement in Multiagent Systems

Given a network of agents, we study the problem of designing a distributed algorithm that computes k independent weighted means of the network's initial conditions (namely, the agents agree on a k-dimensional space). Akin to average consensus, this problem finds applications in distributed computing and sensing, where agents seek to simultaneously evaluate k independent functions at a common point by running a single coordination algorithm. We show that linear algorithms can agree on quantities that are oblique projections of the vector of initial conditions, and we provide techniques to design protocols that are compatible with a pre-specified communication graph. More broadly, our results show that a single agreement algorithm can solve $k$ consensus problems simultaneously at a fraction of the complexity of classical approaches but, in general, it requires higher network connectivity.

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arXiv2024

Approximating Symplectic Realizations: A General Framework for the Construction of Poisson Integrators

While the construction of symplectic integrators for Hamiltonian dynamics is well understood, an analogous general theory for Poisson integrators is still lacking. The main challenge lies in overcoming the singular and non-linear geometric behavior of Poisson structures, such as the presence of symplectic leaves with varying dimensions. In this paper, we propose a general approach for the construction of geometric integrators on any Poisson manifold based on independent geometric and dynamic sources of approximation. The novel geometric approximation is obtained by adapting structural results about symplectic realizations of general Poisson manifolds. We also provide an error analysis for the resulting methods and illustrative applications.

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arXiv2024

Designing Poisson Integrators Through Machine Learning

This paper presents a general method to construct Poisson integrators, i.e., integrators that preserve the underlying Poisson geometry. We assume the Poisson manifold is integrable, meaning there is a known local symplectic groupoid for which the Poisson manifold serves as the set of units. Our constructions build upon the correspondence between Poisson diffeomorphisms and Lagrangian bisections, which allows us to reformulate the design of Poisson integrators as solutions to a certain PDE (Hamilton-Jacobi). The main novelty of this work is to understand the Hamilton-Jacobi PDE as an optimization problem, whose solution can be easily approximated using machine learning related techniques. This research direction aligns with the current trend in the PDE and machine learning communities, as initiated by Physics- Informed Neural Networks, advocating for designs that combine both physical modeling (the Hamilton-Jacobi PDE) and data.

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arXiv2023

Beltrami Fields with Morse Proportionality Factor

In this work we study Beltrami fields with non-constant proportionality factor on $\mathbb{R}^3$. More precisely, we analyze the existence of vector fields $X$ satisfying the equations $curl(X)=fX$ and $div(X)=0$ for a given $f\in C^\infty(\mathbb R^3)$ in a neighborhood of a point $p\in\mathbb{R}^3$. Since the regular case has been treated previously, we focus on the case where $p$ is a non-degenerate critical point of $f$. We prove that for a generic Morse function $f$, the only solution is the trivial one $X\equiv 0$ (here generic refers to explicit arithmetic properties of the eigenvalues of the Hessian of $f$ at $p$). Our results stem from the introduction of algebraic obstructions, which are discussed in detail throughout the paper.

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arXiv2023

Symmetry Preservation in Hamiltonian Systems: Simulation and Learning

This work presents a general geometric framework for simulating and learning the dynamics of Hamiltonian systems that are invariant under a Lie group of transformations. This means that a group of symmetries is known to act on the system respecting its dynamics and, as a consequence, Noether's Theorem, conserved quantities are observed. We propose to simulate and learn the mappings of interest through the construction of $G$-invariant Lagrangian submanifolds, which are pivotal objects in symplectic geometry. A notable property of our constructions is that the simulated/learned dynamics also preserves the same conserved quantities as the original system, resulting in a more faithful surrogate of the original dynamics than non-symmetry aware methods, and in a more accurate predictor of non-observed trajectories. Furthermore, our setting is able to simulate/learn not only Hamiltonian flows, but any Lie group-equivariant symplectic transformation. Our designs leverage pivotal techniques and concepts in symplectic geometry and geometric mechanics: reduction theory, Noether's Theorem, Lagrangian submanifolds, momentum mappings, and coisotropic reduction among others. We also present methods to learn Poisson transformations while preserving the underlying geometry and how to endow non-geometric integrators with geometric properties. Thus, this work presents a novel attempt to harness the power of symplectic and Poisson geometry towards simulating and learning problems.

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arXiv2021

Data-Driven Synthesis of Optimization-Based Controllers for Regulation of Unknown Linear Systems

This paper proposes a data-driven framework to solve time-varying optimization problems associated with unknown linear dynamical systems. Making online control decisions to regulate a dynamical system to the solution of an optimization problem is a central goal in many modern engineering applications. Yet, the available methods critically rely on a precise knowledge of the system dynamics, thus mandating a preliminary system identification phase before a controller can be designed. In this work, we leverage results from behavioral theory to show that the steady-state transfer function of a linear system can be computed from data samples without any knowledge or estimation of the system model. We then use this data-driven representation to design a controller, inspired by a gradient-descent optimization method, that regulates the system to the solution of a convex optimization problem, without requiring any knowledge of the time-varying disturbances affecting the model equation. Results are tailored to cost functions satisfy the Polyak-Łojasiewicz inequality.

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arXiv2021

Online Stochastic Optimization for Unknown Linear Systems: Data-Driven Synthesis and Controller Analysis

This paper proposes a data-driven control framework to regulate an unknown, stochastic linear dynamical system to the solution of a (stochastic) convex optimization problem. Despite the centrality of this problem, most of the available methods critically rely on a precise knowledge of the system dynamics (thus requiring off-line system identification and model refinement). To this aim, in this paper we first show that the steady-state transfer function of a linear system can be computed directly from control experiments, bypassing explicit model identification. Then, we leverage the estimated transfer function to design a controller -- which is inspired by stochastic gradient descent methods -- that regulates the system to the solution of the prescribed optimization problem. A distinguishing feature of our methods is that they do not require any knowledge of the system dynamics, disturbance terms, or their distributions. Our technical analysis combines concepts and tools from behavioral system theory, stochastic optimization with decision-dependent distributions, and stability analysis. We illustrate the applicability of the framework on a case study for mobility-on-demand ride service scheduling in Manhattan, NY.

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arXiv2020

Resource-Aware Discretization of Accelerated Optimization Flows

This paper tackles the problem of discretizing accelerated optimization flows while retaining their convergence properties. Inspired by the success of resource-aware control in developing efficient closed-loop feedback implementations on digital systems, we view the last sampled state of the system as the resource to be aware of. The resulting variable-stepsize discrete-time algorithms retain by design the desired decrease of the Lyapunov certificate of their continuous-time counterparts. Our algorithm design employs various concepts and techniques from resource-aware control that, in the present context, have interesting parallelisms with the discrete-time implementation of optimization algorithms. These include derivative- and performance-based triggers to monitor the evolution of the Lyapunov function as a way of determining the algorithm stepsize, exploiting sampled information to enhance algorithm performance, and employing high-order holds using more accurate integrators of the original dynamics. Throughout the paper, we illustrate our approach on a newly introduced continuous-time dynamics termed heavy-ball dynamics with displaced gradient, but the ideas proposed here have broad applicability to other globally asymptotically stable flows endowed with a Lyapunov certificate.

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arXiv2017

On the Geometry of the Hamilton-Jacobi Equation and Generating Functions

In this paper we develop a geometric version of the Hamilton-Jacobi equation in the Poisson setting. Specifically, we "geometrize" what is usually called a complete solution of the Hamilton-Jacobi equation. We use some well-known results about symplectic groupoids, in particular cotangent groupoids, as a keystone for the construction of our framework. Our methodology follows the ambitious program proposed by A. Weinstein, [62], in order to develop geometric formulations of the dynamical behavior of Lagrangian and Hamiltonian systems on Lie algebroids and Lie groupoids. This procedure allows us to take symmetries into account, and, as a by-product, we recover results from [14, 29, 31], but even in these situations our approach is new. A theory of generating functions for the Poisson structures considered here is also developed following the same pattern, solving a longstanding problem of the area: how to obtain a generating function for the identity tranformation and the nearby Poisson automorphisms of Poisson manifolds. A direct application of our results give the construction of a family of Poisson integrators, that is, integrators that conserve the underlying Poisson geometry. These integrators are implemented in the paper in benchmark problems. Some conclusions, current and future directions of research are shown at the end of the paper.

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arXiv2015

Hamilton-Jacobi theory, Symmetries and Coisotropic Reduction

Reduction theory has played a major role in the study of Hamiltonian systems. On the other hand, the Hamilton-Jacobi theory is one of the main tools to integrate the dynamics of certain Hamiltonian problems and a topic of research on its own. Moreover, the construction of several symplectic integrators rely on approximations of a complete solution of the Hamilton-Jacobi equation. The natural question that we address in this paper is how these two topics (reduction and Hamilton-Jacobi theory) fit together. We obtain a reduction and reconstruction procedure for the Hamilton-Jacobi equation with symmetries, even in a generalized sense to be clarified below. Several applications and relations to other reductions of the Hamilton-Jacobi theory are shown in the last section of the paper. It is remarkable that as a by-product we obtain a generalization of the Ge-Marsden reduction procedure. Quite surprinsingly, the classical ansatzs available in the literature to solve the Hamilton-Jacobi equation are also particular instances of our framework.

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arXiv2014

Hamilton-Jacobi theory in Cauchy data space

Recently, M. de León el al. ([9]) have developed a geometric Hamilton-Jacobi theory for Classical Field Theories in the setting of multisymplectic geometry. Our purpose in the current paper is to establish the corresponding Hamilton-Jacobi theory in the Cauchy data space, and relate both approaches.

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arXiv2012

A Universal Hamilton-Jacobi Theory

In this paper we develop a Hamilton-Jacobi theory in the setting of almost Poisson manifolds. The theory extends the classical Hamilton-Jacobi theory and can be also applied to very general situations including nonholonomic mechanical systems and time dependent systems with external forces.

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