Solving Nonlinear Financial Planning Problems
With 109 Decision Variables On Massively
Parallel Architectures
Author(s)
J. Gondzio & A. Grothey
Abstract
Multistage stochastic programming is a popular technique to deal with uncertainty
in optimization models. However, the need to adequately capture the underlying
distributions leads to large problems that are usually beyond the scope of general
purpose solvers. Dedicated methods exist but pose restrictions on the type of model
they can be applied to. Parallelism makes these problems potentially tractable, but
is generally not exploited in today’s general purpose solvers.
We apply a structure-exploiting parallel primal-dual interior-point solver for
linear, quadratic and nonlinear programming problems. The solver efficiently
exploits the structure of these models. Its design relies on object-oriented
programming principles, treating each substructure of the problem as an object
carrying its own dedicated linear algebra routines.We demonstrate its effectiveness
on a wide range of financial planning problems, resulting in linear, quadratic or
non-linear formulations.
Also coarse grain parallelism is exploited in a generic way that is efficient on
any parallel architecture from ethernet linked PCs to massively parallel computers.
On a 1280-processormachine with a peak performance of 6.2 TFlops we can solve
a quadratic financial planning problem exceeding 109 decision variables.
Keywords: asset and liability management, interior point, massive parallelism,
structure exploitation.
1 Introduction
Decision making under uncertainty is an important consideration in financial
planning. A promising approach to the problem is the multistage stochastic
Keywords
asset and liability management, interior point, massive parallelism,
structure exploitation.
Related Book
Other papers in this volume
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