4 research outputs found
Improving the smoothed complexity of FLIP for max cut problems
Finding locally optimal solutions for max-cut and max--cut are well-known
PLS-complete problems. An instinctive approach to finding such a locally
optimum solution is the FLIP method. Even though FLIP requires exponential time
in worst-case instances, it tends to terminate quickly in practical instances.
To explain this discrepancy, the run-time of FLIP has been studied in the
smoothed complexity framework. Etscheid and R\"{o}glin showed that the smoothed
complexity of FLIP for max-cut in arbitrary graphs is quasi-polynomial. Angel,
Bubeck, Peres, and Wei showed that the smoothed complexity of FLIP for max-cut
in complete graphs is , where is an upper bound on
the random edge-weight density and is the number of vertices in the input
graph.
While Angel et al.'s result showed the first polynomial smoothed complexity,
they also conjectured that their run-time bound is far from optimal. In this
work, we make substantial progress towards improving the run-time bound. We
prove that the smoothed complexity of FLIP in complete graphs is . Our results are based on a carefully chosen matrix whose rank
captures the run-time of the method along with improved rank bounds for this
matrix and an improved union bound based on this matrix. In addition, our
techniques provide a general framework for analyzing FLIP in the smoothed
framework. We illustrate this general framework by showing that the smoothed
complexity of FLIP for max--cut in complete graphs is polynomial and for
max--cut in arbitrary graphs is quasi-polynomial. We believe that our
techniques should also be of interest towards addressing the smoothed
complexity of FLIP for max--cut in complete graphs for larger constants .Comment: 36 page
Superpolynomial smoothed complexity of 3-FLIP in Local Max-Cut
We construct a graph with vertices where the smoothed runtime of the
3-FLIP algorithm for the 3-Opt Local Max-Cut problem can be as large as
. This provides the first example where a local search
algorithm for the Max-Cut problem can fail to be efficient in the framework of
smoothed analysis. We also give a new construction of graphs where the runtime
of the FLIP algorithm for the Local Max-Cut problem is for any
pivot rule. This graph is much smaller and has a simpler structure than
previous constructions.Comment: 18 pages, 3 figure
Search and optimization with randomness in computational economics: equilibria, pricing, and decisions
In this thesis we study search and optimization problems from computational economics with primarily stochastic inputs. The results are grouped into two categories: First, we address the smoothed analysis of Nash equilibrium computation. Second, we address two pricing problems in mechanism design, and solve two economically motivated stochastic optimization problems.
Computing Nash equilibria is a central question in the game-theoretic study of economic systems of agent interactions. The worst-case analysis of this problem has been studied in depth, but little was known beyond the worst case. We study this problem in the framework of smoothed analysis, where adversarial inputs are randomly perturbed. We show that computing Nash equilibria is hard for 2-player games even when input perturbations are large. This is despite the existence of approximation algorithms in a similar regime. In doing so, our result disproves a conjecture relating approximation schemes to smoothed analysis. Despite the hardness results in general, we also present a special case of co-operative games, where we show that the natural greedy algorithm for finding equilibria has polynomial smoothed complexity. We also develop reductions which preserve smoothed analysis.
In the second part of the thesis, we consider optimization problems which are motivated by economic applications. We address two stochastic optimization problems. We begin by developing optimal methods to determine the best among binary classifiers, when the objective function is known only through pairwise comparisons, e.g. when the objective function is the subjective opinion of a client. Finally, we extend known algorithms in the Pandora's box problem --- a classic optimal search problem --- to an order-constrained setting which allows for richer modelling.
The remaining chapters address two pricing problems from mechanism design. First, we provide an approximately revenue-optimal pricing scheme for the problem of selling time on a server to jobs whose parameters are sampled i.i.d. from an unknown distribution. We then tackle the problem of fairly dividing chores among a collection of economic agents via a competitive equilibrium, which balances assigned tasks with payouts. We give efficient algorithms to compute such an equilibrium