1,540 research outputs found
A shooting method for singular nonlinear second order Volterra integro-differential equations
The method of parallel shooting will be employed to solve nonlinear second order singular
Volterra integro-differential equations with two point boundary conditions
Flight Gate Assignment with a Quantum Annealer
Optimal flight gate assignment is a highly relevant optimization problem from
airport management. Among others, an important goal is the minimization of the
total transit time of the passengers. The corresponding objective function is
quadratic in the binary decision variables encoding the flight-to-gate
assignment. Hence, it is a quadratic assignment problem being hard to solve in
general. In this work we investigate the solvability of this problem with a
D-Wave quantum annealer. These machines are optimizers for quadratic
unconstrained optimization problems (QUBO). Therefore the flight gate
assignment problem seems to be well suited for these machines. We use real
world data from a mid-sized German airport as well as simulation based data to
extract typical instances small enough to be amenable to the D-Wave machine. In
order to mitigate precision problems, we employ bin packing on the passenger
numbers to reduce the precision requirements of the extracted instances. We
find that, for the instances we investigated, the bin packing has little effect
on the solution quality. Hence, we were able to solve small problem instances
extracted from real data with the D-Wave 2000Q quantum annealer.Comment: Updated figure
On the Complexity of Searching in Trees: Average-case Minimization
We focus on the average-case analysis: A function w : V -> Z+ is given which
defines the likelihood for a node to be the one marked, and we want the
strategy that minimizes the expected number of queries. Prior to this paper,
very little was known about this natural question and the complexity of the
problem had remained so far an open question.
We close this question and prove that the above tree search problem is
NP-complete even for the class of trees with diameter at most 4. This results
in a complete characterization of the complexity of the problem with respect to
the diameter size. In fact, for diameter not larger than 3 the problem can be
shown to be polynomially solvable using a dynamic programming approach.
In addition we prove that the problem is NP-complete even for the class of
trees of maximum degree at most 16. To the best of our knowledge, the only
known result in this direction is that the tree search problem is solvable in
O(|V| log|V|) time for trees with degree at most 2 (paths).
We match the above complexity results with a tight algorithmic analysis. We
first show that a natural greedy algorithm attains a 2-approximation.
Furthermore, for the bounded degree instances, we show that any optimal
strategy (i.e., one that minimizes the expected number of queries) performs at
most O(\Delta(T) (log |V| + log w(T))) queries in the worst case, where w(T) is
the sum of the likelihoods of the nodes of T and \Delta(T) is the maximum
degree of T. We combine this result with a non-trivial exponential time
algorithm to provide an FPTAS for trees with bounded degree
Semi-autonomous Intersection Collision Avoidance through Job-shop Scheduling
In this paper, we design a supervisor to prevent vehicle collisions at
intersections. An intersection is modeled as an area containing multiple
conflict points where vehicle paths cross in the future. At every time step,
the supervisor determines whether there will be more than one vehicle in the
vicinity of a conflict point at the same time. If there is, then an impending
collision is detected, and the supervisor overrides the drivers to avoid
collision. A major challenge in the design of a supervisor as opposed to an
autonomous vehicle controller is to verify whether future collisions will occur
based on the current drivers choices. This verification problem is particularly
hard due to the large number of vehicles often involved in intersection
collision, to the multitude of conflict points, and to the vehicles dynamics.
In order to solve the verification problem, we translate the problem to a
job-shop scheduling problem that yields equivalent answers. The job-shop
scheduling problem can, in turn, be transformed into a mixed-integer linear
program when the vehicle dynamics are first-order dynamics, and can thus be
solved by using a commercial solver.Comment: Submitted to Hybrid Systems: Computation and Control (HSCC) 201
Secure and linear cryptosystems using error-correcting codes
A public-key cryptosystem, digital signature and authentication procedures
based on a Gallager-type parity-check error-correcting code are presented. The
complexity of the encryption and the decryption processes scale linearly with
the size of the plaintext Alice sends to Bob. The public-key is pre-corrupted
by Bob, whereas a private-noise added by Alice to a given fraction of the
ciphertext of each encrypted plaintext serves to increase the secure channel
and is the cornerstone for digital signatures and authentication. Various
scenarios are discussed including the possible actions of the opponent Oscar as
an eavesdropper or as a disruptor
Phase Transition in the Number Partitioning Problem
Number partitioning is an NP-complete problem of combinatorial optimization.
A statistical mechanics analysis reveals the existence of a phase transition
that separates the easy from the hard to solve instances and that reflects the
pseudo-polynomiality of number partitioning. The phase diagram and the value of
the typical ground state energy are calculated.Comment: minor changes (references, typos and discussion of results
Exploring Graphs with Time Constraints by Unreliable Collections of Mobile Robots
A graph environment must be explored by a collection of mobile robots. Some
of the robots, a priori unknown, may turn out to be unreliable. The graph is
weighted and each node is assigned a deadline. The exploration is successful if
each node of the graph is visited before its deadline by a reliable robot. The
edge weight corresponds to the time needed by a robot to traverse the edge.
Given the number of robots which may crash, is it possible to design an
algorithm, which will always guarantee the exploration, independently of the
choice of the subset of unreliable robots by the adversary? We find the optimal
time, during which the graph may be explored. Our approach permits to find the
maximal number of robots, which may turn out to be unreliable, and the graph is
still guaranteed to be explored.
We concentrate on line graphs and rings, for which we give positive results.
We start with the case of the collections involving only reliable robots. We
give algorithms finding optimal times needed for exploration when the robots
are assigned to fixed initial positions as well as when such starting positions
may be determined by the algorithm. We extend our consideration to the case
when some number of robots may be unreliable. Our most surprising result is
that solving the line exploration problem with robots at given positions, which
may involve crash-faulty ones, is NP-hard. The same problem has polynomial
solutions for a ring and for the case when the initial robots' positions on the
line are arbitrary.
The exploration problem is shown to be NP-hard for star graphs, even when the
team consists of only two reliable robots
Approximating the Minimum Equivalent Digraph
The MEG (minimum equivalent graph) problem is, given a directed graph, to
find a small subset of the edges that maintains all reachability relations
between nodes. The problem is NP-hard. This paper gives an approximation
algorithm with performance guarantee of pi^2/6 ~ 1.64. The algorithm and its
analysis are based on the simple idea of contracting long cycles. (This result
is strengthened slightly in ``On strongly connected digraphs with bounded cycle
length'' (1996).) The analysis applies directly to 2-Exchange, a simple ``local
improvement'' algorithm, showing that its performance guarantee is 1.75.Comment: conference version in ACM-SIAM Symposium on Discrete Algorithms
(1994
Quantum Separability and Entanglement Detection via Entanglement-Witness Search and Global Optimization
We focus on determining the separability of an unknown bipartite quantum
state by invoking a sufficiently large subset of all possible
entanglement witnesses given the expected value of each element of a set of
mutually orthogonal observables. We review the concept of an entanglement
witness from the geometrical point of view and use this geometry to show that
the set of separable states is not a polytope and to characterize the class of
entanglement witnesses (observables) that detect entangled states on opposite
sides of the set of separable states. All this serves to motivate a classical
algorithm which, given the expected values of a subset of an orthogonal basis
of observables of an otherwise unknown quantum state, searches for an
entanglement witness in the span of the subset of observables. The idea of such
an algorithm, which is an efficient reduction of the quantum separability
problem to a global optimization problem, was introduced in PRA 70 060303(R),
where it was shown to be an improvement on the naive approach for the quantum
separability problem (exhaustive search for a decomposition of the given state
into a convex combination of separable states). The last section of the paper
discusses in more generality such algorithms, which, in our case, assume a
subroutine that computes the global maximum of a real function of several
variables. Despite this, we anticipate that such algorithms will perform
sufficiently well on small instances that they will render a feasible test for
separability in some cases of interest (e.g. in 3-by-3 dimensional systems)
A scheduling theory framework for GPU tasks efficient execution
Concurrent execution of tasks in GPUs can reduce the computation time of a workload by
overlapping data transfer and execution commands.
However it is difficult to implement an efficient run-
time scheduler that minimizes the workload makespan
as many execution orderings should be evaluated. In
this paper, we employ scheduling theory to build a
model that takes into account the device capabili-
ties, workload characteristics, constraints and objec-
tive functions. In our model, GPU tasks schedul-
ing is reformulated as a flow shop scheduling prob-
lem, which allow us to apply and compare well known
methods already developed in the operations research
field. In addition we develop a new heuristic, specif-
ically focused on executing GPU commands, that
achieves better scheduling results than previous tech-
niques. Finally, a comprehensive evaluation, showing
the suitability and robustness of this new approach,
is conducted in three different NVIDIA architectures
(Kepler, Maxwell and Pascal).Proyecto TIN2016- 0920R, Universidad de Málaga (Campus de Excelencia Internacional Andalucía Tech) y programa de donación de NVIDIA Corporation
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