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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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111222333444 · May 202619922001200920172026
48 results for Control Theory

We develop an epsilon-controlled algebraic L-theory, extending our earlier work on epsilon-controlled algebraic K-theory. The controlled L-theory is very close to being a generalized homology theory; we study analogues of the homology exact sequence of a pair, excision properties, and the Mayer--Vietoris exact sequence…

2004-02-13abs ↗pdf ↗

Abstract: Surveying connections between ML and Control Theory.

problem Addressing the intersection of Machine Learning and Control Theory.
method Develops connections through reinforcement learning, supervised learning, deep learning, and stochastic gradient descent.
result Machine Learning and Control Theory are interconnected, with ML solving large control problems and Control Theory providing tools for ML.

This paper provides a full controlled version of algebraic KK-theory. This includes a rich array of assembly maps; the controlled assembly isomorphism theorem identifying the controlled group with homology; and the stability theorem describing the behavior of the inverse limit as the control parameter goes to 0. There…

2004-02-24abs ↗pdf ↗

Survey on statistical learning theory for control, focusing on linear systems.

problem Applying machine learning techniques to control systems, especially linear ones.
method Adapting tools from modern high-dimensional statistics and learning theory.
result Recent advances in statistical learning theory for control, particularly for linear systems.

Some simple examples from quantum physics and control theory are used to illustrate the application of the theory of Lie systems. We will show, in particular, that for certain physical models both of the corresponding classical and quantum problems can be treated in a similar way, may be up to the replacement of the in…

2003-05-09abs ↗pdf ↗

Unified control theory and machine learning for safety in uncertain systems.

problem Safety guarantees for systems with measurement model uncertainty.
method Measurement-Robust Control Barrier Functions (MR-CBFs) for control synthesis.
result MR-CBFs ensure safety in perception systems with measurement model uncertainty.

The abstract discusses convergent realizations of Lie subalgebras in control theory.

problem Characterizing Lie subalgebras that can be realized as convergent vector fields.
method Generalizations and reformulations of algebraic properties for output realization.
result Recovery and clarification of previous results on control-affine systems and realization of Chen-Fliess series.

Subject of present paper is the review of results of authors on foliation theory and applications of foliation theory in control systems. The paper consists of two parts. In the first part the results of authors on foliation theory are presented, in the second part the results on applications of foliation theory in the…

2012-04-04abs ↗pdf ↗

We prove a squeezing/stability theorem for delta-epsilon controlled L-groups when the control map is a fibration on a finite polyhedron. A relation with boundedly-controlled L-groups is also discussed.

2004-02-13abs ↗pdf ↗

Just as an explicit parameterisation of system dynamics by state, i.e., a choice of coordinates, can impede the identification of general structure, so it is too with an explicit parameterisation of system dynamics by control. However, such explicit and fixed parameterisation by control is commonplace in control theory…

2013-12-23abs ↗pdf ↗

The paper analyzes deep neural networks using control theory to set a time limit for their convergence.

problem Understanding the finite-time convergence of deep neural networks.
method Lyapunov based analysis of the loss function, control theory framework, finite-time control of non-linear systems.
result A priori guarantees of finite-time convergence for deep neural networks are provided.

New theory for nonsmooth systems helps optimize and control complex functions.

problem Optimizing and controlling systems with nonsmooth functions.
method Higher-order averaging theory with nonsmooth near-identity transformation and lexicographic differentiation.
result Closed formula for nonsmooth first and second-order averaging.

The paper tackles Neyman-Pearson classification control issues.

problem Neyman-Pearson classification's control constraint is hard to satisfy in finite samples.
method Developed refined learning procedures under two accuracy control strategies.
result Proposed methods achieve desired control levels in finite samples.

Gradient descent on LSE objectives implicitly performs EM, leading to collapse without volume control.

problem Gradient collapse in autoencoders without volume control.
method Introduced a single-layer encoder with an LSE objective and InfoMax regularization for volume control.
result Gradient--responsibility identity holds exactly; LSE alone collapses; variance prevents dead components; decorrelation prevents redundancy.

Optimal control theory connects diffusion models to generative modeling.

problem Sampling from unnormalized densities in statistics and computational sciences.
method Deriving a Hamilton-Jacobi-Bellman equation and applying control theory to minimize Kullback-Leibler divergence.
result Time-reversed diffusion sampler (DIS) outperforms other diffusion-based sampling methods.

Optimizes electric field to control molecule states in Hartree-Fock theory.

problem Optimizing electric field to drive molecule from initial to target state.
method Trust region optimization with gradients from adjoint state method.
result Achieves desired target states with minimal control effort.

Since governments give stimulus to firms and expect the spillover effect by fiscal policies, it is important to know the effectiveness that they can control the economy. To clarify the controllability of the economy, we investigate a firm production network observed exhaustively in Japan and what firms should be direct…

2016-04-05abs ↗pdf ↗

Deep residual networks can approximate any continuous function using control theory.

problem Universal approximation capabilities of deep residual neural networks.
method Relating residual networks to control systems and using Lie algebraic techniques.
result Deep residual networks with adequately deep layers can approximate any continuous function on a compact set.

Survey of theoretical foundations for policy optimization in control.

problem Understanding the theoretical properties of gradient-based methods in control and reinforcement learning.
method Interdisciplinary review of optimization landscape, convergence, and sample complexity for various control problems.
result Recent theoretical results on stability and robustness in learning-based control.

Proves spectral inequality and null-controllability for elliptic operators on closed manifolds.

problem Proving spectral inequalities and null-controllability for elliptic pseudo-differential operators.
method Periodization approach in time inspired by global pseudo-differential calculus.
result Established spectral inequality and null-controllability for elliptic operators on closed manifolds.

New control theory for self-path-dependent problems solves unique constraints.

problem Optimal control with self-path-dependent constraints in stochastic systems.
method Introduces new HJB equations for variational inequalities with historical maximum controls.
result Value functions are viscosity solutions to HJB equations under Lipschitz conditions.

Motion planning and control are key problems in a collection of robotic applications including the design of autonomous agile vehicles and of minimalist manipulators. These problems can be accurately formalized within the language of affine connections and of geometric control theory. In this paper we overview recent r…

2002-09-17abs ↗pdf ↗

Machine and reinforcement learning (RL) are increasingly being applied to plan and control the behavior of autonomous systems interacting with the physical world. Examples include self-driving vehicles, distributed sensor networks, and agile robots. However, when machine learning is to be applied in these new settings,…

2019-06-27abs ↗pdf ↗

Study proves existence of multiple geodesics in a specific metric space.

problem Existence of multiple geodesics in a manifold with a Randers-Kropina metric.
method Lusternik-Schnirelman theory applied to a homotopy type of solutions of an affine control system.
result Proves existence of infinitely many geodesics between two points in a non-contractible manifold.

The study sets limits on how well systems can be controlled adaptively.

problem Learning to control unknown linear Gaussian systems with quadratic costs.
method Combining ideas from experiment design, estimation theory, and perturbation bounds of information matrices.
result Regret lower bounds of the order of T\sqrt{T} in the time horizon TT accurately capture control-theoretic parameters.

Motivated by the ubiquity of control-affine systems in optimal control theory, we investigate the geometry of point-affine control systems with metric structures in dimensions two and three. We compute local isometric invariants for point-affine distributions of constant type with metric structures for systems with 2 s…

2012-06-06abs ↗pdf ↗

A general study of symmetries in optimal control theory is given, starting from the presymplectic description of this kind of system. Then, Noether's theorem, as well as the corresponding reduction procedure (based on the application of the Marsden-Weinstein theorem adapted to the presymplectic case) are stated both in…

2002-06-20abs ↗pdf ↗

Survey of recent developments in symmetric reductions and controls for Hamiltonian systems.

problem Understanding the internal relationships of geometric structures and controls in Hamiltonian systems with symmetry.
method Survey and introduction of recent developments in controlled Hamiltonian systems with symmetry.
result Reveals the relationships between geometric structures, nonholonomic constraints, dynamical vector fields, and controls.

We solve a continuous-time game-theoretic problem for Kihlstrom-Mirman preferences.

problem Dynamic inconsistency in preferences due to multiattribute utility theory.
method Formalized an equilibrium control theory for continuous-time Markov processes.
result Equilibrium strategy and value function as solution to extended HJB system.

End-to-end algorithm for controlling bilinear systems with probabilistic noise.

problem Controlling bilinear systems with noisy data.
method Proposes an end-to-end algorithm using statistical learning theory and robust controller design.
result Derived finite sample identification error bounds and structurally suitable for control.

Controller seeks informative system observations to predict nonlinear dynamics.

problem Predicting nonlinear dynamics with uncertain parameters.
method Expected free energy minimization for balancing goal state and informative observations.
result Controller improves performance in uncertain parameter scenarios.

Geometric framework for dynamic feedback linearization of control systems with symmetry.

problem Dynamic feedback linearization of control systems with symmetry.
method Geometric framework based on Lie symmetry, systematic procedure for all smooth, generic system trajectories.
result Sufficient condition for dynamic feedback linearizability obtained.

New proof of generalized Chow-Rashevskii theorem for non-linear systems.

problem Generalized Chow-Rashevskii Theorem for non-linear systems.
method Independent proof structure allowing generalizations to orbits of compositions of flows.
result Proof structure applicable to applications in Control Theory and controllability criteria.

Control of drawdown, that is, the control of the drops in wealth over time from peaks to subsequent lows, is of great concern from a risk management perspective. With this motivation in mind, the focal point of this paper is to address the drawdown issue in a stock trading context. Although our analysis can be carried …

2017-10-04abs ↗pdf ↗

This manuscript surveys reinforcement learning from the perspective of optimization and control with a focus on continuous control applications. It surveys the general formulation, terminology, and typical experimental implementations of reinforcement learning and reviews competing solution paradigms. In order to compa…

2018-06-25abs ↗pdf ↗

New method uses neural networks to solve complex PDEs from optimal control theory.

problem Solving high-dimensional Hamilton-Jacobi-Bellman PDEs.
method Iterative diffusion optimization techniques, focusing on path measures and divergences.
result Favourable properties of log-variance divergence for Monte Carlo estimators.