4 research outputs found
Solving equilibrium problems in economies with financial markets, home production, and retention
We propose a new methodology to compute equilibria for general equilibrium
problems on exchange economies with real financial markets, home-production,
and retention. We demonstrate that equilibrium prices can be determined by
solving a related maxinf-optimization problem. We incorporate the non-arbitrage
condition for financial markets into the equilibrium formulation and establish
the equivalence between solutions to both problems. This reduces the complexity
of the original by eliminating the need to directly compute financial contract
prices, allowing us to calculate equilibria even in cases of incomplete
financial markets.
We also introduce a Walrasian bifunction that captures the imbalances and
show that maxinf-points of this function correspond to equilibrium points.
Moreover, we demonstrate that every equilibrium point can be approximated by a
limit of maxinf points for a family of perturbed problems, by relying on the
notion of lopsided convergence.
Finally, we propose an augmented Walrasian algorithm and present numerical
examples to illustrate the effectiveness of this approach. Our methodology
allows for efficient calculation of equilibria in a variety of exchange
economies and has potential applications in finance and economics
An Algorithmic Theory of Dependent Regularizers, Part 1: Submodular Structure
We present an exploration of the rich theoretical connections between several
classes of regularized models, network flows, and recent results in submodular
function theory. This work unifies key aspects of these problems under a common
theory, leading to novel methods for working with several important models of
interest in statistics, machine learning and computer vision.
In Part 1, we review the concepts of network flows and submodular function
optimization theory foundational to our results. We then examine the
connections between network flows and the minimum-norm algorithm from
submodular optimization, extending and improving several current results. This
leads to a concise representation of the structure of a large class of pairwise
regularized models important in machine learning, statistics and computer
vision.
In Part 2, we describe the full regularization path of a class of penalized
regression problems with dependent variables that includes the graph-guided
LASSO and total variation constrained models. This description also motivates a
practical algorithm. This allows us to efficiently find the regularization path
of the discretized version of TV penalized models. Ultimately, our new
algorithms scale up to high-dimensional problems with millions of variables