12,116 research outputs found
Generalized Schwarzschild's method
We describe a new finite element method (FEM) to construct continuous
equilibrium distribution functions of stellar systems. The method is a
generalization of Schwarzschild's orbit superposition method from the space of
discrete functions to continuous ones. In contrast to Schwarzschild's method,
FEM produces a continuous distribution function (DF) and satisfies the intra
element continuity and Jeans equations. The method employs two finite-element
meshes, one in configuration space and one in action space. The DF is
represented by its values at the nodes of the action-space mesh and by
interpolating functions inside the elements. The Galerkin projection of all
equations that involve the DF leads to a linear system of equations, which can
be solved for the nodal values of the DF using linear or quadratic programming,
or other optimization methods. We illustrate the superior performance of FEM by
constructing ergodic and anisotropic equilibrium DFs for spherical stellar
systems (Hernquist models). We also show that explicitly constraining the DF by
the Jeans equations leads to smoother and/or more accurate solutions with both
Schwarzschild's method and FEM.Comment: 14 pages, 7 Figures, Submitted to MNRA
A Bayesian approach to constrained single- and multi-objective optimization
This article addresses the problem of derivative-free (single- or
multi-objective) optimization subject to multiple inequality constraints. Both
the objective and constraint functions are assumed to be smooth, non-linear and
expensive to evaluate. As a consequence, the number of evaluations that can be
used to carry out the optimization is very limited, as in complex industrial
design optimization problems. The method we propose to overcome this difficulty
has its roots in both the Bayesian and the multi-objective optimization
literatures. More specifically, an extended domination rule is used to handle
objectives and constraints in a unified way, and a corresponding expected
hyper-volume improvement sampling criterion is proposed. This new criterion is
naturally adapted to the search of a feasible point when none is available, and
reduces to existing Bayesian sampling criteria---the classical Expected
Improvement (EI) criterion and some of its constrained/multi-objective
extensions---as soon as at least one feasible point is available. The
calculation and optimization of the criterion are performed using Sequential
Monte Carlo techniques. In particular, an algorithm similar to the subset
simulation method, which is well known in the field of structural reliability,
is used to estimate the criterion. The method, which we call BMOO (for Bayesian
Multi-Objective Optimization), is compared to state-of-the-art algorithms for
single- and multi-objective constrained optimization
Distributed Partitioned Big-Data Optimization via Asynchronous Dual Decomposition
In this paper we consider a novel partitioned framework for distributed
optimization in peer-to-peer networks. In several important applications the
agents of a network have to solve an optimization problem with two key
features: (i) the dimension of the decision variable depends on the network
size, and (ii) cost function and constraints have a sparsity structure related
to the communication graph. For this class of problems a straightforward
application of existing consensus methods would show two inefficiencies: poor
scalability and redundancy of shared information. We propose an asynchronous
distributed algorithm, based on dual decomposition and coordinate methods, to
solve partitioned optimization problems. We show that, by exploiting the
problem structure, the solution can be partitioned among the nodes, so that
each node just stores a local copy of a portion of the decision variable
(rather than a copy of the entire decision vector) and solves a small-scale
local problem
Solving one-dimensional unconstrained global optimization problem using parameter free filled function method
It is generally known that almost all filled function methods for one-dimensional unconstrained global optimization problems have computational weaknesses. This paper introduces a relatively new parameter free filled function, which creates a non-ascending bridge from any local isolated minimizer to other first local isolated minimizer with lower or equal function value. The algorithm’s unprecedented function can be used to determine all extreme and inflection points between the two considered consecutive local isolated minimizers. The proposed method never fails to carry out its job. The results of the several testing examples have shown the capability and efficiency of this algorithm while at the same time, proving that the computational weaknesses of the filled function methods can be overcomed
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