22 research outputs found
Strongly polynomial algorithm for a class of minimum-cost flow problems with separable convex objectives
A well-studied nonlinear extension of the minimum-cost flow problem is to
minimize the objective over feasible flows ,
where on every arc of the network, is a convex function. We give
a strongly polynomial algorithm for the case when all 's are convex
quadratic functions, settling an open problem raised e.g. by Hochbaum [1994].
We also give strongly polynomial algorithms for computing market equilibria in
Fisher markets with linear utilities and with spending constraint utilities,
that can be formulated in this framework (see Shmyrev [2009], Devanur et al.
[2011]). For the latter class this resolves an open question raised by Vazirani
[2010]. The running time is for quadratic costs,
for Fisher's markets with linear utilities and
for spending constraint utilities.
All these algorithms are presented in a common framework that addresses the
general problem setting. Whereas it is impossible to give a strongly polynomial
algorithm for the general problem even in an approximate sense (see Hochbaum
[1994]), we show that assuming the existence of certain black-box oracles, one
can give an algorithm using a strongly polynomial number of arithmetic
operations and oracle calls only. The particular algorithms can be derived by
implementing these oracles in the respective settings
A strongly polynomial algorithm for linear exchange markets
We present a strongly polynomial algorithm for computing an equilibrium in Arrow-Debreu exchange markets with linear utilities. Our algorithm is based on a variant of the weakly-polynomial Duan–Mehlhorn (DM) algorithm. We use the DM algorithm as a subroutine to identify revealed edges, i.e. pairs of agents and goods that must correspond to best bang-per-buck transactions in every equilibrium solution. Every time a new revealed edge is found, we use another subroutine that decides if there is an optimal solution using the current set of revealed edges, or if none exists, finds the solution that approximately minimizes the violation of the demand and supply constraints. This task can be reduced to solving a linear program (LP). Even though we are unable to solve this LP in strongly polynomial time, we show that it can be approximated by a simpler LP with two variables per inequality that is solvable in strongly polynomial time
A probabilistic tree-based representation for non-convex minimum cost flow problems
Network flow optimisation has many real-world applications. The minimum cost flow problem (MCFP) is one of the most common network flow problems. Mathematical programming methods often assume the linearity and convexity of the underlying cost function, which is not realistic in many real-world situations. Solving large-sized MCFPs with nonlinear non-convex cost functions poses a much harder problem. In this paper, we propose a new representation scheme for solving non-convex MCFPs using genetic algorithms (GAs). The most common representation scheme for solving the MCFP in the literature using a GA is priority-based encoding, but it has some serious limitations including restricting the search space to a small part of the feasible set. We introduce a probabilistic tree-based representation scheme (PTbR) that is far superior compared to the priority-based encoding. Our extensive experimental investigations show the advantage of our encoding compared to previous methods for a variety of cost functions
Ascending-Price Algorithms for Unknown Markets
We design a simple ascending-price algorithm to compute a
-approximate equilibrium in Arrow-Debreu exchange markets with
weak gross substitute (WGS) property, which runs in time polynomial in market
parameters and . This is the first polynomial-time
algorithm for most of the known tractable classes of Arrow-Debreu markets,
which is easy to implement and avoids heavy machinery such as the ellipsoid
method. In addition, our algorithm can be applied in unknown market setting
without exact knowledge about the number of agents, their individual utilities
and endowments. Instead, our algorithm only relies on queries to a global
demand oracle by posting prices and receiving aggregate demand for goods as
feedback. When demands are real-valued functions of prices, the oracles can
only return values of bounded precision based on real utility functions. Due to
this more realistic assumption, precision and representation of prices and
demands become a major technical challenge, and we develop new tools and
insights that may be of independent interest. Furthermore, our approach also
gives the first polynomial-time algorithm to compute an exact equilibrium for
markets with spending constraint utilities, a piecewise linear concave
generalization of linear utilities. This resolves an open problem posed by Duan
and Mehlhorn (2015).Comment: 33 page