215,922 research outputs found
A Convergent Approximation of the Pareto Optimal Set for Finite Horizon Multiobjective Optimal Control Problems (MOC) Using Viability Theory
The objective of this paper is to provide a convergent numerical
approximation of the Pareto optimal set for finite-horizon multiobjective
optimal control problems for which the objective space is not necessarily
convex. Our approach is based on Viability Theory. We first introduce the
set-valued return function V and show that the epigraph of V is equal to the
viability kernel of a properly chosen closed set for a properly chosen
dynamics. We then introduce an approximate set-valued return function with
finite set-values as the solution of a multiobjective dynamic programming
equation. The epigraph of this approximate set-valued return function is shown
to be equal to the finite discrete viability kernel resulting from the
convergent numerical approximation of the viability kernel proposed in [4, 5].
As a result, the epigraph of the approximate set-valued return function
converges towards the epigraph of V. The approximate set-valued return function
finally provides the proposed numerical approximation of the Pareto optimal set
for every initial time and state. Several numerical examples are provided
A system of ODEs for a Perturbation of a Minimal Mass Soliton
We study soliton solutions to a nonlinear Schrodinger equation with a
saturated nonlinearity. Such nonlinearities are known to possess minimal mass
soliton solutions. We consider a small perturbation of a minimal mass soliton,
and identify a system of ODEs similar to those from Comech and Pelinovsky
(2003), which model the behavior of the perturbation for short times. We then
provide numerical evidence that under this system of ODEs there are two
possible dynamical outcomes, which is in accord with the conclusions of
Pelinovsky, Afanasjev, and Kivshar (1996). For initial data which supports a
soliton structure, a generic initial perturbation oscillates around the stable
family of solitons. For initial data which is expected to disperse, the finite
dimensional dynamics follow the unstable portion of the soliton curve.Comment: Minor edit
Parameterized complexity of machine scheduling: 15 open problems
Machine scheduling problems are a long-time key domain of algorithms and
complexity research. A novel approach to machine scheduling problems are
fixed-parameter algorithms. To stimulate this thriving research direction, we
propose 15 open questions in this area whose resolution we expect to lead to
the discovery of new approaches and techniques both in scheduling and
parameterized complexity theory.Comment: Version accepted to Computers & Operations Researc
Review of Summation-by-parts schemes for initial-boundary-value problems
High-order finite difference methods are efficient, easy to program, scales
well in multiple dimensions and can be modified locally for various reasons
(such as shock treatment for example). The main drawback have been the
complicated and sometimes even mysterious stability treatment at boundaries and
interfaces required for a stable scheme. The research on summation-by-parts
operators and weak boundary conditions during the last 20 years have removed
this drawback and now reached a mature state. It is now possible to construct
stable and high order accurate multi-block finite difference schemes in a
systematic building-block-like manner. In this paper we will review this
development, point out the main contributions and speculate about the next
lines of research in this area
Solving Dynamic Discrete Choice Models Using Smoothing and Sieve Methods
We propose to combine smoothing, simulations and sieve approximations to
solve for either the integrated or expected value function in a general class
of dynamic discrete choice (DDC) models. We use importance sampling to
approximate the Bellman operators defining the two functions. The random
Bellman operators, and therefore also the corresponding solutions, are
generally non-smooth which is undesirable. To circumvent this issue, we
introduce a smoothed version of the random Bellman operator and solve for the
corresponding smoothed value function using sieve methods. We show that one can
avoid using sieves by generalizing and adapting the `self-approximating' method
of Rust (1997) to our setting. We provide an asymptotic theory for the
approximate solutions and show that they converge with root-N-rate, where
is number of Monte Carlo draws, towards Gaussian processes. We examine their
performance in practice through a set of numerical experiments and find that
both methods perform well with the sieve method being particularly attractive
in terms of computational speed and accuracy
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