17 research outputs found
Multi-Objective Model Checking of Markov Decision Processes
We study and provide efficient algorithms for multi-objective model checking
problems for Markov Decision Processes (MDPs). Given an MDP, M, and given
multiple linear-time (\omega -regular or LTL) properties \varphi\_i, and
probabilities r\_i \epsilon [0,1], i=1,...,k, we ask whether there exists a
strategy \sigma for the controller such that, for all i, the probability that a
trajectory of M controlled by \sigma satisfies \varphi\_i is at least r\_i. We
provide an algorithm that decides whether there exists such a strategy and if
so produces it, and which runs in time polynomial in the size of the MDP. Such
a strategy may require the use of both randomization and memory. We also
consider more general multi-objective \omega -regular queries, which we
motivate with an application to assume-guarantee compositional reasoning for
probabilistic systems.
Note that there can be trade-offs between different properties: satisfying
property \varphi\_1 with high probability may necessitate satisfying \varphi\_2
with low probability. Viewing this as a multi-objective optimization problem,
we want information about the "trade-off curve" or Pareto curve for maximizing
the probabilities of different properties. We show that one can compute an
approximate Pareto curve with respect to a set of \omega -regular properties in
time polynomial in the size of the MDP.
Our quantitative upper bounds use LP methods. We also study qualitative
multi-objective model checking problems, and we show that these can be analysed
by purely graph-theoretic methods, even though the strategies may still require
both randomization and memory.Comment: 21 pages, 2 figure
Distance Oracles for Time-Dependent Networks
We present the first approximate distance oracle for sparse directed networks
with time-dependent arc-travel-times determined by continuous, piecewise
linear, positive functions possessing the FIFO property.
Our approach precomputes approximate distance summaries from
selected landmark vertices to all other vertices in the network. Our oracle
uses subquadratic space and time preprocessing, and provides two sublinear-time
query algorithms that deliver constant and approximate
shortest-travel-times, respectively, for arbitrary origin-destination pairs in
the network, for any constant . Our oracle is based only on
the sparsity of the network, along with two quite natural assumptions about
travel-time functions which allow the smooth transition towards asymmetric and
time-dependent distance metrics.Comment: A preliminary version appeared as Technical Report ECOMPASS-TR-025 of
EU funded research project eCOMPASS (http://www.ecompass-project.eu/). An
extended abstract also appeared in the 41st International Colloquium on
Automata, Languages, and Programming (ICALP 2014, track-A
An Upper Bound on the Number of Extreme Shortest Paths in Arbitrary Dimensions
Graphs with multiple edge costs arise naturally in the route planning domain when apart from travel time other criteria like fuel consumption or positive height difference are also objectives to be minimized. In such a scenario, this paper investigates the number of extreme shortest paths between a given source-target pair s, t. We show that for a fixed but arbitrary number of cost types d ? 1 the number of extreme shortest paths is in n^O(log^{d-1}n) in graphs G with n nodes. This is a generalization of known upper bounds for d = 2 and d = 3
Combinatorial Contracts Beyond Gross Substitutes
We study the combinatorial contracting problem of D\"utting et al. [FOCS
'21], in which a principal seeks to incentivize an agent to take a set of
costly actions. In their model, there is a binary outcome (the agent can
succeed or fail), and the success probability and the costs depend on the set
of actions taken. The optimal contract is linear, paying the agent an
fraction of the reward. For gross substitutes (GS) rewards and additive costs,
they give a poly-time algorithm for finding the optimal contract. They use the
properties of GS functions to argue that there are poly-many "critical values"
of , and that one can iterate through all of them efficiently in order
to find the optimal contract.
In this work we study to which extent GS rewards and additive costs
constitute a tractability frontier for combinatorial contracts. We present an
algorithm that for any rewards and costs, enumerates all critical values, with
poly-many demand queries (in the number of critical values). This implies the
tractability of the optimal contract for any setting with poly-many critical
values and efficient demand oracle. A direct corollary is a poly-time algorithm
for the optimal contract in settings with supermodular rewards and submodular
costs. We also study a natural class of matching-based instances with XOS
rewards and additive costs. While the demand problem for this setting is
tractable, we show that it admits an exponential number of critical values. On
the positive side, we present (pseudo-) polynomial-time algorithms for two
natural special cases of this setting. Our work unveils a profound connection
to sensitivity analysis, and designates matching-based instances as a crucial
focal point for gaining a deeper understanding of combinatorial contract
settings.Comment: 22 pages, 3 figure
Shadows of Newton Polytopes
We define the shadow complexity of a polytope P as the maximum number of vertices in a linear projection of P to the plane. We describe connections to algebraic complexity and to parametrized optimization. We also provide several basic examples and constructions, and develop tools for bounding shadow complexity
Analysis of FPTASes for the Multi-Objective Shortest Path Problem
We propose a new FPTAS for the multi-objective shortest path problem. The algorithm uses elements from both an exact labeling algorithm and an FPTAS proposed by Tsaggouris and Zaroliagis (2009). We analyze the running times of these three algorithms both from a the- oretical and a computational point of view. Theoretically, we show that there are instances for which the new FPTAS runs an arbitrary times faster than the other two algorithms. Fur- thermore, for the bi-objective case, the number of approximate solutions generated by the proposed FPTAS is at most the number of Pareto-optimal solutions multiplied by the number of nodes. By performing a set of computational tests, we show that the new FPTAS performs best in terms of running ti
Near-Quadratic Lower Bounds for Two-Pass Graph Streaming Algorithms
We prove that any two-pass graph streaming algorithm for the -
reachability problem in -vertex directed graphs requires near-quadratic
space of bits. As a corollary, we also obtain near-quadratic space
lower bounds for several other fundamental problems including maximum bipartite
matching and (approximate) shortest path in undirected graphs.
Our results collectively imply that a wide range of graph problems admit
essentially no non-trivial streaming algorithm even when two passes over the
input is allowed. Prior to our work, such impossibility results were only known
for single-pass streaming algorithms, and the best two-pass lower bounds only
ruled out space algorithms, leaving open a large gap between
(trivial) upper bounds and lower bounds