4,301 research outputs found
A dynamic gradient approach to Pareto optimization with nonsmooth convex objective functions
In a general Hilbert framework, we consider continuous gradient-like
dynamical systems for constrained multiobjective optimization involving
non-smooth convex objective functions. Our approach is in the line of a
previous work where was considered the case of convex di erentiable objective
functions. Based on the Yosida regularization of the subdi erential operators
involved in the system, we obtain the existence of strong global trajectories.
We prove a descent property for each objective function, and the convergence of
trajectories to weak Pareto minima. This approach provides a dynamical
endogenous weighting of the objective functions. Applications are given to
cooperative games, inverse problems, and numerical multiobjective optimization
Quantum Multiobservable Control
We present deterministic algorithms for the simultaneous control of an
arbitrary number of quantum observables. Unlike optimal control approaches
based on cost function optimization, quantum multiobservable tracking control
(MOTC) is capable of tracking predetermined homotopic trajectories to target
expectation values in the space of multiobservables. The convergence of these
algorithms is facilitated by the favorable critical topology of quantum control
landscapes. Fundamental properties of quantum multiobservable control
landscapes that underlie the efficiency of MOTC, including the multiobservable
controllability Gramian, are introduced. The effects of multiple control
objectives on the structure and complexity of optimal fields are examined. With
minor modifications, the techniques described herein can be applied to general
quantum multiobjective control problems.Comment: To appear in Physical Review
An efficient method for multiobjective optimal control and optimal control subject to integral constraints
We introduce a new and efficient numerical method for multicriterion optimal
control and single criterion optimal control under integral constraints. The
approach is based on extending the state space to include information on a
"budget" remaining to satisfy each constraint; the augmented
Hamilton-Jacobi-Bellman PDE is then solved numerically. The efficiency of our
approach hinges on the causality in that PDE, i.e., the monotonicity of
characteristic curves in one of the newly added dimensions. A semi-Lagrangian
"marching" method is used to approximate the discontinuous viscosity solution
efficiently. We compare this to a recently introduced "weighted sum" based
algorithm for the same problem. We illustrate our method using examples from
flight path planning and robotic navigation in the presence of friendly and
adversarial observers.Comment: The final version accepted by J. Comp. Math. : 41 pages, 14 figures.
Since the previous version: typos fixed, formatting improved, one mistake in
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A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems
In this article we propose a descent method for equality and inequality
constrained multiobjective optimization problems (MOPs) which generalizes the
steepest descent method for unconstrained MOPs by Fliege and Svaiter to
constrained problems by using two active set strategies. Under some regularity
assumptions on the problem, we show that accumulation points of our descent
method satisfy a necessary condition for local Pareto optimality. Finally, we
show the typical behavior of our method in a numerical example
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