Study of Sard problem in Carnot groups using dynamical systems.
problem Sard problem in sub-Riemannian Carnot groups.
method Dynamical-systems approach to study singular curves.
result Positively answer the Sard problem in some Carnot groups.
On-device research index
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.
169,291 papers · 148 categories
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48 results for “dynamic problems”
Solves portfolio optimization with costs using numerical methods.
problem Dynamic portfolio optimization with transaction costs and constraints.
method Numerical dynamic programming techniques.
result Problems can now be solved tractably.
Machine learning infers time-reversible dynamics from data.
problem Learn time-reversible dynamics constrained by initial and final conditions.
method Machine learning algorithms solve boundary value problems for deterministic and stochastic dynamics.
result Inferred time-reversible dynamics for various types of systems.
Researchers tackle the globalization problem of locally cosymplectic Hamiltonian dynamics.
problem Globalization problem of locally cosymplectic Hamiltonian dynamics.
method Investigate the geometry of locally conformally cosymplectic manifolds and provide a geometric Hamilton-Jacobi theory.
result Provide a geometric Hamilton-Jacobi theory on locally conformally cosymplectic manifolds.
Bayesian optimization adapted for dynamic problems with improved efficiency.
problem Optimizing functions that change over time.
method Spatiotemporal Gaussian process priors and adaptive evaluation strategy.
result Improves efficiency in tracking and optimizing dynamic functions.
Dynamic programming for optimal stopping under distribution constraints.
problem Optimal stopping with distributional constraints.
method Reformulating as measure-valued martingales and stochastic control problem.
result Established dynamic programming principle.
This paper optimizes a dynamic portfolio using novel dynamic programming.
problem Maximizing a portfolio's value over time with changing prices.
method Novel theoretical approach based on dynamic programming for both deterministic and stochastic cases.
result Theoretical approach successfully maximizes portfolio value using dynamic programming.
In this paper, we analyze dynamic programming as a novel approach to solve the problem of maximizing the profits of a bank. The mathematical model of the problem and the description of a bank's work is described in this paper. The problem is then approached using the method of dynamic programming. Dynamic programming m…
Paper models non-linear dynamics from time series data.
problem Modeling non-linear dynamical systems from time series data.
method Introduces latent state modeling and a novel alternating minimization algorithm.
result LaNoLem achieves competitive performance in dynamics estimation and prediction.
Reinforcement learning would enjoy better success on real-world problems if domain knowledge could be imparted to the algorithm by the modelers. Most problems have both hidden state and unknown dynamics. Partially observable Markov decision processes (POMDPs) allow for the modeling of both. Unfortunately, they do not p…
The paper addresses time inconsistency in mean-risk optimization.
problem Time inconsistency in mean-risk optimization.
method Use of a time consistent dynamic convex risk measure to evaluate portfolio risk.
result The dynamic mean-risk problem satisfies a set-valued Bellman's principle.
Develops a new method for risk diversification using dynamic risk measures.
problem Dynamic risk diversification in investment portfolios.
method Introduces dynamic risk contributions and a recursive optimization approach for coherent dynamic distortion risk measures.
result Dynamic risk budgeting strategies can be solved using deep learning.
Dynamic characterization of barycentric optimal transport problems and their martingale relaxationmath.PR
Extends optimal transport to dynamic and martingale settings.
problem Dynamic and martingale relaxation of optimal transport problems.
method Extends Benamou-Brenier formula to weak optimal transport and introduces barycentric optimal transport.
result Relates barycentric optimal transport to martingale Benamou-Brenier formula.
Representation Results for Law Invariant Recursive Dynamic Deviation Measures and Risk Sharingq-fin.RM
Paper analyzes dynamic deviation measures and risk-sharing solutions.
problem Optimal risk-sharing solutions for dynamic deviation measures.
method Dynamic inf-convolution problem involving transformed dynamic deviation measures.
result The only dynamic deviation measure that is law invariant and recursive is variance.
New algorithm improves convergence for non-convex problems with boundaries.
problem Optimizing non-convex problems with constraints.
method Reflected Gradient Langevin Dynamics with probabilistic representation.
result Promising convergence rates, faster than existing methods.
GNL addresses dynamic network regression by learning dynamic graph structures and capturing sequence information.
problem Dynamic network regression of multiple inter-connected data entities.
method Graph Neural Lasso (GNL) using gated diffusive units and attention mechanism.
result GNL outperforms existing methods in dynamic network regression tasks.
This paper develops methods to solve saddle-point problems on Riemannian manifolds with exponential stability.
problem Solving saddle-point problems on Riemannian manifolds with exponential stability.
method Developed a projected dynamical system on a Riemannian manifold to solve saddle-point problems, leveraging the strong monotonicity of the gradient of the Lagrangian function.
result Established exponential stability and convergence of the projected dynamical system to the unique saddle-point.
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problemscs.LG
A new dynamic attention model improves vehicle routing problem solutions.
problem Vehicle routing problems (VRP) are NP-hard and challenging to solve.
method Dynamic attention model with a dynamic encoder-decoder architecture.
result The model outperforms previous methods and shows good generalization.
Paper analyzes convergence of dynamic policy gradient for MDPs, improving performance in finite-time problems.
problem Optimal policies in finite-time MDPs are not stationary and require epoch-specific training.
method Introduces dynamic policy gradient combining dynamic programming and policy gradient, analyzes convergence for softmax parametrisation.
result Dynamic policy gradient training exploits finite-time structure, leading to better convergence bounds.
Paper unifies subspace identification and DMD for dynamical systems.
problem Estimating dynamical models from data.
method Unified optimization and regression problems for SID and DMD.
result Proves equivalence of SID and DMD for optimal model construction.
Novel algorithm for optimal control of nonlinear systems.
problem Optimal control of nonlinear stochastic dynamical systems with unknown dynamics.
method Decoupled data-based approach combining open-loop and closed-loop control.
result Performance of D2C algorithm is approximately optimal and significantly reduces training time.
New machine learning pipeline solves dynamic vehicle routing problems efficiently.
problem Efficiently handling same day deliveries in e-commerce logistics.
method Combination of machine learning and combinatorial optimization.
result Ranked first in the EURO Meets NeurIPS Vehicle Routing Competition.
Parametric identification of the dynamics of inter-sectoral balance: modelling and forecastingmath.DS
Develops methods to model and forecast inter-sectoral balance dynamics.
problem Modeling and forecasting the dynamics of inter-sectoral balance in macroeconomic systems.
method Approach to specification and identification of a weakly formalized dynamical system, matching procedure for parameters, detection of significant harmonic waves.
result Effective methods for detecting and modeling significant harmonic waves in macroeconomic systems.
The paper simplifies utility maximization problems using dynamic convex duality.
problem Constrained utility maximization problems.
method Formulating primal and dual problems, using FBSDEs, and characterizing optimal controls.
result Explicit dynamic characterization of optimal controls and wealth processes.
This paper tackles dynamic ensemble selection and data preprocessing for multi-class imbalance learning.
problem Class imbalance in multi-class datasets where majority class has more instances.
method Examined five preprocessing methods and four dynamic selection methods for multi-class imbalanced problems.
result Dynamic ensemble improves F-measure and G-mean compared to static ensemble.
Study on 4-body problem with inverse cube force potential.
problem Equal mass planar 4-body problem with inverse cube force potential.
method Reparametrizes dynamics as geodesics of a metric and examines curvature in reduced space.
result Derives dynamical consequences and proves a numerical conjecture.
Two heuristics solve dynamic multiple travelling salesmen problems.
problem Dynamic routing with unknown customers.
method Balanced dynamic closest vehicle heuristic and balanced dynamic assignment vehicle heuristic.
result Continuous approximation models for strategic dynamic routing.
Community-based system dynamics improves ML fairness by involving excluded stakeholders.
problem Bias in ML system development during problem formulation.
method Community-based system dynamics (CBSD) for stakeholder participation.
result CBSD facilitates deeper problem understanding and bias mitigation.
This paper analyzes dynamic ensemble selection and preprocessing for multi-class imbalanced datasets.
problem Class imbalance in multi-class datasets where majority classes have more instances.
method Examined dynamic selection techniques and data preprocessing methods for multi-class imbalanced problems.
result Dynamic ensemble improves AUC and G-mean compared to static ensemble.
New algorithm for dynamic -means clustering in high dimensions.
problem Dynamic clustering of high-dimensional data points.
method One-pass coreset construction algorithm for -means.
result Nearly optimal space usage for dynamic -means.
Universal online optimization for dynamic environments using uniclass prediction.
problem Online optimization in changing environments with dynamic regret.
method Reduces dynamic online optimization to uniclass prediction problem, allowing control over dynamic regret bounds.
result First paper with state-of-the-art dynamic regret guarantees for general convex cost functions.
HRM-Agent: Training a recurrent reasoning model in dynamic environments using reinforcement learningcs.AI
HRM-Agent learns to navigate dynamic mazes using reinforcement learning.
problem Training HRM in dynamic, uncertain, partially observable environments.
method Reinforcement learning to train HRM-Agent.
result HRM-Agent successfully learns to navigate dynamic mazes.
Study minimax rates for online learning with time-varying dynamics.
problem Online learning with time-varying state and cost dynamics.
method Non-constructive upper and lower bounds, complexity and stability terms.
result Characterization of minimax rates and necessary conditions for learnability.
Efficiently tunes hyperparameters with dynamic accuracy method.
problem Optimizing machine learning hyperparameters with inexact evaluations.
method Dynamic accuracy derivative-free optimization for hyperparameter tuning.
result Demonstrates robust and efficient hyperparameter tuning compared to fixed accuracy methods.
Algorithm selects best model based on state, reducing costs.
problem Choosing the best model among many in different states of the world.
method Reinforcement learning algorithm to estimate optimal policy.
result Algorithm consistently selects optimal model based on covariates.
Model dynamic customer sensitivities across categories.
problem Dynamic heterogeneity in customer sensitivities to marketing elements.
method Hierarchical dynamic factor model with Bayesian nonparametric Gaussian processes.
result Dynamic heterogeneity can be explained by a few global trends.
New algorithm adapts to unknown demand smoothness for dynamic pricing.
problem Dynamic pricing with unknown Hölder smoothness of demand function.
method Self-similarity condition and adaptive algorithm.
result Adaptive algorithm achieves minimax optimal regret without prior knowledge of smoothness.
DPDP combines neural heuristics with DP for vehicle routing problems.
problem Vehicle routing problems with large scale.
method Deep Policy Dynamic Programming (DPDP) that uses a neural network policy to prioritize and restrict the DP state space.
result DPDP improves upon classical DP algorithms and outperforms neural approaches for TSP, VRP, and TSPTW.
Problem of global integration of geometric structures arising in the theory of dynamical systems admitting the normal shift is considered. In the case when such integration is possible the problem of globalization for shift maps is studied.
LQG for portfolio optimizationq-fin.PM
We solve dynamic portfolio allocation with LQG for predictable markets.
problem Dynamic portfolio allocation with market predictability, price impact, and partial observability.
method LQG framework for linear state-space models, deriving optimal control policy.
result Existence and uniqueness of optimal controller linked to non-arbitrage criterion.
Solves optimal stopping problem for financial technical analysis.
problem Optimal stopping problem for technical analysis models.
method Wide-class dynamics modeling support/resistance lines.
result Solution to optimal stopping problem for technical analysis.
A neural network solves dictionary learning problems efficiently.
problem Solving dictionary learning problems efficiently.
method Combining top-down feedback and contrastive learning with spiking neurons.
result True gradients for learning are provably computable by individual neurons.
MaxCOSD algorithm tackles non-i.i.d. demands and stateful dynamics in online inventory control.
problem Managing inventory with non-i.i.d. demands and stateful dynamics.
method MaxCOSD, an online algorithm with provable guarantees for non-degeneracy assumptions.
result MaxCOSD achieves optimal performance for non-i.i.d. demands and stateful dynamics.
New setup for online learning captures continuous changes in losses, improving dynamic regret analysis.
problem Capturing regularity in online learning problems with continuous changes in losses.
method Introducing Continuous Online Learning (COL) and proving its equivalence to solving certain equilibrium problems (EPs).
result Achieving sublinear dynamic regret in COL is equivalent to solving certain EPs, offering conditions for efficient algorithms.
Algorithm learns dynamics from past observations.
problem Learning a nonlinear dynamical system.
method Spectral filtering, online convex optimization.
result Vanishing prediction error for marginally stable systems.
Model stock price dynamics using semi-Markov processes.
problem Model stock price dynamics through a semi-Markov process.
method Use semi-Markov process with Poisson random measure, establish existence and uniqueness of solution, derive HJB equation.
result Obtain expressions for optimal controls and value function using HJB equation.
Geometric Hydrodynamics tackles open problems in fluid dynamics.
problem Open problems in fluid dynamics and invariant metrics.
method Variational settings, models for invariant metrics, Cauchy and boundary value problems.
result New constructions and recent developments in fluid dynamics.
New algorithm reduces performance loss in IRL with mismatched transition dynamics.
problem Performance degradation in inverse reinforcement learning due to mismatched transition dynamics.
method Proposed a robust Maximum Causal Entropy (MCE) IRL algorithm leveraging robust reinforcement learning insights.
result Empirically demonstrated stable performance improvement under transition dynamics mismatches.