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New LEQG Covariance Steering Problem Analyzed in Continuous Time

Researchers have formulated and analyzed the linear exponential quadratic Gaussian (LEQG) covariance steering problem in continuous time. This problem can be viewed as a risk-sensitive Schrödinger bridge between Gaussian endpoints within a linear quadratic framework. The LEQG covariance steering controller, a linear state feedback, is not available in closed form, unlike its risk-neutral counterpart. The optimal controller is defined by a symmetric matrix that solves an algebraic equation reflecting the risk-sensitivity parameter's influence. AI

IMPACT This research advances theoretical frameworks in control theory, potentially influencing future AI systems that require sophisticated risk-sensitive decision-making.

RANK_REASON The cluster contains an academic paper detailing a new mathematical formulation and analysis of a control problem. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New LEQG Covariance Steering Problem Analyzed in Continuous Time

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The cluster contains an academic paper detailing a new mathematical formulation and analysis of a control problem. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Chiran B. Cherian, Yasemin Isik, Abhishek Halder ·

    Linear Exponential Quadratic Gaussian Covariance Steering

    arXiv:2609.12463v1 Announce Type: cross Abstract: We formulate and analyze the linear exponential quadratic Gaussian (LEQG) covariance steering problem in continuous time over a given deadline (finite time horizon). The solution for this problem can be seen as a risk-sensitive Sc…