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   Articles with "value functions" as a keyword



Generalized derivatives of optimal-value functions with parameterized convex programs embedded

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recommendations! Published in 2024 at "Journal of Global Optimization"

DOI: 10.1007/s10898-023-01359-9

Abstract: This article proposes new practical methods for furnishing generalized derivative information of optimal-value functions with embedded parameterized convex programs, with potential applications in nonsmooth equation-solving and optimization. We consider three cases of parameterized convex programs:… read more here.

Keywords: optimal value; parameterized convex; generalized derivatives; value functions ... See more keywords

Two Optimal Value Functions in Parametric Conic Linear Programming

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recommendations! Published in 2021 at "Journal of Optimization Theory and Applications"

DOI: 10.1007/s10957-021-01959-z

Abstract: We consider the conic linear program given by a closed convex cone in an Euclidean space and a matrix, where vector on the right-hand-side of the constraint system and the vector defining the objective function… read more here.

Keywords: optimal value; value functions; value; two optimal ... See more keywords

Automatic Pan–Tilt Camera Control for Learning Dirichlet Process Gaussian Process (DPGP) Mixture Models of Multiple Moving Targets

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recommendations! Published in 2019 at "IEEE Transactions on Automatic Control"

DOI: 10.1109/tac.2018.2849584

Abstract: Information value functions based on the Kullback–Leibler (KL) divergence have been shown the most effective for planning sensor measurements by means of greedy strategies. The problem of optimizing information value over a finite time horizon… read more here.

Keywords: information value; process; value functions; control ... See more keywords

Robust Losses for Learning Value Functions

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recommendations! Published in 2022 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"

DOI: 10.1109/tpami.2022.3213503

Abstract: Most value function learning algorithms in reinforcement learning are based on the mean squared (projected) Bellman error. However, squared errors are known to be sensitive to outliers, both skewing the solution of the objective and… read more here.

Keywords: learning value; losses learning; bellman error; robust losses ... See more keywords

Code and Data Repository for Construction of Value Functions of Integer Programs with Finite Domain

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recommendations! Published in 2025 at "INFORMS Journal on Computing"

DOI: 10.1287/ijoc.2024.0757.cd

Abstract: Value functions play a central role in integer programming duality, and they are also used to develop solution methods for stochastic integer programs, bilevel integer programs and robust optimization problems. In this paper, we propose… read more here.

Keywords: functions integer; algorithm; programs finite; value functions ... See more keywords