276,433 research outputs found
Compact Toroidal Ion Trap Design and Optimization
We present the design of a new type of compact toroidal, or "halo", ion trap.
Such traps may be useful for mass spectrometry, studying small Coulomb cluster
rings, quantum information applications, or other quantum simulations where a
ring topology is of interest. We present results from a Monte Carlo
optimization of the trap design parameters using finite-element analysis
simulations that minimizes higher-order anharmonic terms in the trapping
pseudopotential, while maintaining complete control over ion placement at the
pseudopotential node in 3D using static bias fields. These simulations are
based on a practical electrode design using readily-available parts, yet can be
easily scaled to any size trap with similar electrode spacings. We also derive
the conditions for a crystal phase transition for two ions in the compact halo
trap, the first non-trivial phase transition for Coulomb crystals in this
geometry.Comment: 8 pages, 9 figure
On limited-memory quasi-Newton methods for minimizing a quadratic function
The main focus in this paper is exact linesearch methods for minimizing a
quadratic function whose Hessian is positive definite. We give two classes of
limited-memory quasi-Newton Hessian approximations that generate search
directions parallel to those of the method of preconditioned conjugate
gradients, and hence give finite termination on quadratic optimization
problems. The Hessian approximations are described by a novel compact
representation which provides a dynamical framework. We also discuss possible
extensions of these classes and show their behavior on randomly generated
quadratic optimization problems. The methods behave numerically similar to
L-BFGS. Inclusion of information from the first iteration in the limited-memory
Hessian approximation and L-BFGS significantly reduces the effects of round-off
errors on the considered problems. In addition, we give our compact
representation of the Hessian approximations in the full Broyden class for the
general unconstrained optimization problem. This representation consists of
explicit matrices and gradients only as vector components
Generic identifiability and second-order sufficiency in tame convex optimization
We consider linear optimization over a fixed compact convex feasible region
that is semi-algebraic (or, more generally, "tame"). Generically, we prove that
the optimal solution is unique and lies on a unique manifold, around which the
feasible region is "partly smooth", ensuring finite identification of the
manifold by many optimization algorithms. Furthermore, second-order optimality
conditions hold, guaranteeing smooth behavior of the optimal solution under
small perturbations to the objective
Coalition structure generation in cooperative games with compact representations
This paper presents a new way of formalizing the coalition structure generation problem (CSG) so that we can apply constraint optimization techniques to it. Forming effective coalitions is a major research challenge in AI and multi-agent systems. CSG involves partitioning a set of agents into coalitions to maximize social surplus. Traditionally, the input of the CSG problem is a black-box function called a characteristic function, which takes a coalition as input and returns the value of the coalition. As a result, applying constraint optimization techniques to this problem has been infeasible. However, characteristic functions that appear in practice often can be represented concisely by a set of rules, rather than treating the function as a black box. Then we can solve the CSG problem more efficiently by directly applying constraint optimization techniques to this compact representation. We present new formalizations of the CSG problem by utilizing recently developed compact representation schemes for characteristic functions. We first characterize the complexity of CSG under these representation schemes. In this context, the complexity is driven more by the number of rules than by the number of agents. As an initial step toward developing efficient constraint optimization algorithms for solving the CSG problem, we also develop mixed integer programming formulations and show that an off-the-shelf optimization package can perform reasonably well
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