6,586 research outputs found
Adapting the interior point method for the solution of LPs on serial, coarse grain parallel and massively parallel computers
In this paper we describe a unified scheme for implementing an interior point algorithm (IPM) over a range of computer architectures. In the inner iteration of the IPM a search direction is computed using Newton's method. Computationally this involves solving a sparse symmetric positive definite (SSPD) system of equations. The choice of direct and indirect methods for the solution of this system, and the design of data structures to take advantage of serial, coarse grain parallel and massively parallel computer architectures, are considered in detail. We put forward arguments as to why integration of the system within a sparse simplex solver is important and outline how the system is designed to achieve this integration
Parameter Selection and Pre-Conditioning for a Graph Form Solver
In a recent paper, Parikh and Boyd describe a method for solving a convex
optimization problem, where each iteration involves evaluating a proximal
operator and projection onto a subspace. In this paper we address the critical
practical issues of how to select the proximal parameter in each iteration, and
how to scale the original problem variables, so as the achieve reliable
practical performance. The resulting method has been implemented as an
open-source software package called POGS (Proximal Graph Solver), that targets
multi-core and GPU-based systems, and has been tested on a wide variety of
practical problems. Numerical results show that POGS can solve very large
problems (with, say, more than a billion coefficients in the data), to modest
accuracy in a few tens of seconds. As just one example, a radiation treatment
planning problem with around 100 million coefficients in the data can be solved
in a few seconds, as compared to around one hour with an interior-point method.Comment: 28 pages, 1 figure, 1 open source implementatio
Solution of partial differential equations on vector and parallel computers
The present status of numerical methods for partial differential equations on vector and parallel computers was reviewed. The relevant aspects of these computers are discussed and a brief review of their development is included, with particular attention paid to those characteristics that influence algorithm selection. Both direct and iterative methods are given for elliptic equations as well as explicit and implicit methods for initial boundary value problems. The intent is to point out attractive methods as well as areas where this class of computer architecture cannot be fully utilized because of either hardware restrictions or the lack of adequate algorithms. Application areas utilizing these computers are briefly discussed
Euclidean distance geometry and applications
Euclidean distance geometry is the study of Euclidean geometry based on the
concept of distance. This is useful in several applications where the input
data consists of an incomplete set of distances, and the output is a set of
points in Euclidean space that realizes the given distances. We survey some of
the theory of Euclidean distance geometry and some of the most important
applications: molecular conformation, localization of sensor networks and
statics.Comment: 64 pages, 21 figure
Sparse Equation-Eigen Solvers for Symmetric/Unsymmetric Positive-Negative-Indefinite Matrices with Finite Element and Linear Programming Applications
Vectorized sparse solvers for direct solutions of positive-negative-indefinite symmetric systems of linear equations and eigen-equations are developed. Sparse storage schemes, re-ordering, symbolic factorization and numerical factorization algorithms are discussed. Loop unrolling techniques are also incorporated in the coding to enhance the vector speed. In the indefinite solver, which employs various pivoting strategies, a simple rotation matrix is introduced to simplify the computer implementation. Efficient usage of the incore memory is accomplished by the proposed restart memory management schemes. A sparse version of the Interior Point Method, IPM, has also been implemented that incorporates the developed indefinite sparse solver for linear programming applications.
Numerical performance of the developed software is conducted by performing the static analysis and eigen-analysis of several practical finite elements models, such as the EXXON Offshore Structure, the High Speed Civil Transport (HSCT) Aircraft, and the Space Shuttle Solid Rocket Booster (SRB). The results have been compared to benchmark results provided by the Computational Structural Branch at NASA Langley Research Center. Small to medium-scale linear programming examples have also been used to demonstrate the robustness of the proposed sparse IPM
Revisiting Implicit Differentiation for Learning Problems in Optimal Control
This paper proposes a new method for differentiating through optimal
trajectories arising from non-convex, constrained discrete-time optimal control
(COC) problems using the implicit function theorem (IFT). Previous works solve
a differential Karush-Kuhn-Tucker (KKT) system for the trajectory derivative,
and achieve this efficiently by solving an auxiliary Linear Quadratic Regulator
(LQR) problem. In contrast, we directly evaluate the matrix equations which
arise from applying variable elimination on the Lagrange multiplier terms in
the (differential) KKT system. By appropriately accounting for the structure of
the terms within the resulting equations, we show that the trajectory
derivatives scale linearly with the number of timesteps. Furthermore, our
approach allows for easy parallelization, significantly improved scalability
with model size, direct computation of vector-Jacobian products and improved
numerical stability compared to prior works. As an additional contribution, we
unify prior works, addressing claims that computing trajectory derivatives
using IFT scales quadratically with the number of timesteps. We evaluate our
method on a both synthetic benchmark and four challenging, learning from
demonstration benchmarks including a 6-DoF maneuvering quadrotor and 6-DoF
rocket powered landing.Comment: Accepted to NeurIPS 2023 (poster
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