17 research outputs found
Index Reduction for Differential-Algebraic Equations with Mixed Matrices
Differential-algebraic equations (DAEs) are widely used for modeling of
dynamical systems. The difficulty in solving numerically a DAE is measured by
its differentiation index. For highly accurate simulation of dynamical systems,
it is important to convert high-index DAEs into low-index DAEs. Most of
existing simulation software packages for dynamical systems are equipped with
an index-reduction algorithm given by Mattsson and S\"{o}derlind.
Unfortunately, this algorithm fails if there are numerical cancellations.
These numerical cancellations are often caused by accurate constants in
structural equations. Distinguishing those accurate constants from generic
parameters that represent physical quantities, Murota and Iri introduced the
notion of a mixed matrix as a mathematical tool for faithful model description
in structural approach to systems analysis. For DAEs described with the use of
mixed matrices, efficient algorithms to compute the index have been developed
by exploiting matroid theory.
This paper presents an index-reduction algorithm for linear DAEs whose
coefficient matrices are mixed matrices, i.e., linear DAEs containing physical
quantities as parameters. Our algorithm detects numerical cancellations between
accurate constants, and transforms a DAE into an equivalent DAE to which
Mattsson--S\"{o}derlind's index-reduction algorithm is applicable. Our
algorithm is based on the combinatorial relaxation approach, which is a
framework to solve a linear algebraic problem by iteratively relaxing it into
an efficiently solvable combinatorial optimization problem. The algorithm does
not rely on symbolic manipulations but on fast combinatorial algorithms on
graphs and matroids. Furthermore, we provide an improved algorithm under an
assumption based on dimensional analysis of dynamical systems.Comment: A preliminary version of this paper is to appear in Proceedings of
the Eighth SIAM Workshop on Combinatorial Scientific Computing, Bergen,
Norway, June 201
Data-Driven Projection for Reducing Dimensionality of Linear Programs: Generalization Bound and Learning Methods
This paper studies a simple data-driven approach to high-dimensional linear
programs (LPs). Given data of past -dimensional LPs, we learn an
\textit{projection matrix} (), which reduces the dimensionality from
to . Then, we address future LP instances by solving -dimensional LPs and
recovering -dimensional solutions by multiplying the projection matrix. This
idea is compatible with any user-preferred LP solvers, hence a versatile
approach to faster LP solving. One natural question is: how much data is
sufficient to ensure the recovered solutions' quality? We address this question
based on the idea of \textit{data-driven algorithm design}, which relates the
amount of data sufficient for generalization guarantees to the
\textit{pseudo-dimension} of performance metrics. We present an
upper bound on the pseudo-dimension
( compresses logarithmic factors) and complement it by an
lower bound, hence tight up to an factor.
On the practical side, we study two natural methods for learning projection
matrices: PCA- and gradient-based methods. While the former is simple and
efficient, the latter sometimes leads to better solution quality. Experiments
confirm that learned projection matrices are beneficial for reducing the time
for solving LPs while maintaining high solution quality
Faster Discrete Convex Function Minimization with Predictions: The M-Convex Case
Recent years have seen a growing interest in accelerating optimization
algorithms with machine-learned predictions. Sakaue and Oki (NeurIPS 2022) have
developed a general framework that warm-starts the L-convex function
minimization method with predictions, revealing the idea's usefulness for
various discrete optimization problems. In this paper, we present a framework
for using predictions to accelerate M-convex function minimization, thus
complementing previous research and extending the range of discrete
optimization algorithms that can benefit from predictions. Our framework is
particularly effective for an important subclass called laminar convex
minimization, which appears in many operations research applications. Our
methods can improve time complexity bounds upon the best worst-case results by
using predictions and even have potential to go beyond a lower-bound result
Algebraic combinatorial optimization on the degree of determinants of noncommutative symbolic matrices
We address the computation of the degrees of minors of a noncommutative
symbolic matrix of form where are matrices over a
field , are noncommutative variables, are integer
weights, and is a commuting variable specifying the degree. This problem
extends noncommutative Edmonds' problem (Ivanyos et al. 2017), and can
formulate various combinatorial optimization problems. Extending the study by
Hirai 2018, and Hirai, Ikeda 2022, we provide novel duality theorems and
polyhedral characterization for the maximum degrees of minors of of all
sizes, and develop a strongly polynomial-time algorithm for computing them.
This algorithm is viewed as a unified algebraization of the classical Hungarian
method for bipartite matching and the weight-splitting algorithm for linear
matroid intersection. As applications, we provide polynomial-time algorithms
for weighted fractional linear matroid matching and linear optimization over
rank-2 Brascamp-Lieb polytopes