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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.

168,695 papers · 148 categories

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97195292389 · Jun 202019922001200920172026
48 results for cost control

Non-bilinear observations make optimal control harder, showing non-convex costs and non-affine optimal controllers.

problem Optimal control from bilinear observations in linear systems is challenging.
method Analytical and numerical methods to study the non-convex cost-to-go and non-affine optimal controllers.
result The Separation Principle does not hold for bilinear observations, leading to non-convex costs and non-affine optimal controllers.

FavMac maximizes value while controlling cost in multi-label prediction.

problem Value-maximizing predictions with strict cost control in multi-label scenarios.
method FavMac pipeline combining any multi-label classifier with online update mechanism.
result FavMac achieves higher value with strict cost control compared to baselines.

New method for handling multi-dimensional singular controls with jump costs in mean-field problems.

problem Handling jump costs in multi-dimensional singular controls.
method Introducing two-layer parametrisations to interpolate jumps on both distributional and pathwise levels.
result Derivation of a DPP and characterisation of the value function as a minimal super-solution to a quasi-variational inequality.

Study cost-driven state representation learning for control from partial observations.

problem Learning state representation for control from partial and high-dimensional observations.
method Cost-driven state representation learning via predicting cumulative costs.
result Established finite-sample guarantees for near-optimal representation and controller.

Study learns state representations from observations for control, proving guarantees.

problem Learning state representations from high-dimensional observations for control.
method Cost-driven approach, learning latent state model to predict costs.
result Proves finite-sample guarantees for near-optimal state representation and controller.

Optimal control in changing systems without strong convexity assumptions.

problem Adversarial changes in convex costs for unknown linear systems.
method Non-convex lower confidence bounds and computationally-efficient regret minimization.
result Achieves T\smash{\sqrt{T}}-regret rate, optimal compared to best stabilizing controller.

Study optimal control of diffusion processes with infimum or supremum costs.

problem Optimizing control of a diffusion process with costs dependent on its infimum or supremum.
method Introduced novel integral operators to solve two-dimensional singular control problems.
result Explicit solutions for optimal dividend problem with time-dependent preferences.

Paper tackles online control of linear systems with unbounded noise.

problem Online control of linear systems under unbounded noise with unknown convex cost functions.
method Developed an algorithm achieving ildeO(T) ilde{O}(\sqrt{T}) high-probability regret under unbounded noise, and established O(mpoly(logT)) O({ m poly} (\log T)) regret bound for strongly convex costs and sub-Gaussian noise.
result Achieved ildeO(T) ilde{O}(\sqrt{T}) high-probability regret under unbounded noise, and O(mpoly(logT)) O({ m poly} (\log T)) regret bound for specific noise and cost conditions.

New method handles robust and adaptive control of linear systems with non-convex costs.

problem Robust and adaptive control of linear systems with unknown parameters.
method Combining non-asymptotic linear regression, interval prediction, and tree-based planning.
result First end-to-end suboptimality analysis for robust and adaptive MPC with non-convex costs.

The paper bridges stochastic control and deep hedging for European call options with transaction costs.

problem Hedging and pricing European call options with proportional transaction costs.
method Complementary perspectives: stochastic control and deep hedging. Two architectures proposed: NTBN-Delta and WW-NTBN.
result WW-NTBN converges faster, matches no-transaction bands more closely, and generalizes well across transaction cost regimes.

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.

New algorithm achieves logarithmic regret for adversarial online control.

problem Online linear-quadratic control in systems with adversarial disturbances.
method Characterization of optimal offline control law, reduced to online learning with approximate advantage functions.
result First algorithm with logarithmic regret for arbitrary adversarial disturbance sequences.

A new cost-frugal HPO method controls training cost during optimization.

problem Ignoring training cost variation in HPO leads to inefficient hyperparameter tuning.
method Developed a randomized direct-search method with convergence and approximation guarantees.
result Proved an O(dK)O(\frac{\sqrt{d}}{\sqrt{K}}) convergence rate and O(dε2)O(dε^{-2}) approximation guarantee.

The paper teaches robots to navigate by learning costs from expert demonstrations.

problem Teaching robots to navigate autonomously using only expert observations.
method Developed a map encoder and cost encoder to infer semantic class probabilities and a cost function from expert observations.
result Robots can learn to follow traffic rules in a simulator using only semantic observations.

New method disentangles perceptual uncertainty and behavioral costs in partially observable systems.

problem Tackles inverse optimal control for non-linear partially observable systems.
method Probabilistic approach using maximum causal entropy formulations and local linearization.
result Disentangles perceptual factors and behavioral costs in sequential decision-making.

Study portfolio optimization with transaction costs and recursive preferences.

problem Optimizing portfolios under transaction costs and recursive preferences.
method Recursive preferences, transaction costs, and Merton investment-consumption problem.
result Characterized all parameter combinations for well-posedness of the problem.

Consider the problem of a central bank that wants to manage the exchange rate between its domestic currency and a foreign one. The central bank can purchase and sell the foreign currency, and each intervention on the exchange market leads to a proportional cost whose instantaneous marginal value depends on the current …

2017-12-06abs ↗pdf ↗

Combines cost-sensitive and Neyman-Pearson paradigms for better binary classification.

problem Asymmetric binary classification problems with unequal error severities.
method Develops TUBE-CS algorithm to bridge cost-sensitive and Neyman-Pearson paradigms.
result High-probability control of population type I error.

Optimizes costs in uncertain Markov systems using risk filters.

problem Optimizing costs in systems with model uncertainty and unknown parameters.
method Risk filters and Bellman principle of optimality applied to Bayesian framework.
result Derives the Bellman principle for non-standard risk-averse control problems.

Greenhouse environment is the key to influence crops production. However, it is difficult for classical control methods to give precise environment setpoints, such as temperature, humidity, light intensity and carbon dioxide concentration for greenhouse because it is uncertain nonlinear system. Therefore, an intelligen…

2019-12-01abs ↗pdf ↗

This paper deals with discrete-time Markov control processes on a general state space. A long-run risk-sensitive average cost criterion is used as a performance measure. The one-step cost function is nonnegative and possibly unbounded. Using the vanishing discount factor approach, the optimality inequality and an optim…

2007-04-03abs ↗pdf ↗

Study explores optimal portfolio control in financial markets with transaction costs.

problem Optimal portfolio control in financial markets with proportional transaction costs.
method Geometric approach to financial markets, set-valued techniques, stochastic Mayer control problem.
result Continuity of the optimal value and control under price approximations in a multi-asset framework.

The paper develops a method to learn navigation costs from expert demonstrations in partially observable environments.

problem Learning navigation costs from expert demonstrations in partially observable environments.
method Develops a cost function representation composed of a probabilistic occupancy encoder and a cost encoder, optimized by differentiating the error between demonstrated controls and a control policy computed from the cost encoder.
result The method outperforms baseline IRL algorithms in robot navigation tasks, improving both training and test-time efficiency.

In the early phases of the product life cycle, the costs controls became a major decision tool in the competitiveness of the companies due to the world competition. After defining the problems related to this control difficulties, we will present an approach using a concept of cost entity related to the design and real…

2010-11-26abs ↗pdf ↗

We consider Online Convex Optimization (OCO) in the setting where the costs are mm-strongly convex and the online learner pays a switching cost for changing decisions between rounds. We show that the recently proposed Online Balanced Descent (OBD) algorithm is constant competitive in this setting, with competitive rat…

2018-10-23abs ↗pdf ↗

Study optimal investment and consumption strategies with various transaction costs.

problem Investment and consumption decisions under varying transaction costs.
method Dynamic programming and singular perturbation expansion for small cost-to-wealth ratio.
result Derive leading-order asymptotic formulas for no-trade regions and trading boundaries.

MPC framework reduces execution costs and schedule deviations in trading.

problem Executing large orders in markets under time and liquidity constraints.
method Model Predictive Control (MPC) framework balancing order completion, market impact, and opportunity cost.
result Significant reductions in slippage and schedule shortfall compared to benchmarks.

This paper is concerned with cost optimization of an insurance company. The surplus of the insurance company is modeled by a controlled regime switching diffusion, where the regime switching mechanism provides the fluctuations of the random environment. The goal is to find an optimal control that minimizes the total co…

2010-09-16abs ↗pdf ↗