231,672 research outputs found
The Alternating Stock Size Problem and the Gasoline Puzzle
Given a set S of integers whose sum is zero, consider the problem of finding
a permutation of these integers such that: (i) all prefix sums of the ordering
are nonnegative, and (ii) the maximum value of a prefix sum is minimized.
Kellerer et al. referred to this problem as the "Stock Size Problem" and showed
that it can be approximated to within 3/2. They also showed that an
approximation ratio of 2 can be achieved via several simple algorithms.
We consider a related problem, which we call the "Alternating Stock Size
Problem", where the number of positive and negative integers in the input set S
are equal. The problem is the same as above, but we are additionally required
to alternate the positive and negative numbers in the output ordering. This
problem also has several simple 2-approximations. We show that it can be
approximated to within 1.79.
Then we show that this problem is closely related to an optimization version
of the gasoline puzzle due to Lov\'asz, in which we want to minimize the size
of the gas tank necessary to go around the track. We present a 2-approximation
for this problem, using a natural linear programming relaxation whose feasible
solutions are doubly stochastic matrices. Our novel rounding algorithm is based
on a transformation that yields another doubly stochastic matrix with special
properties, from which we can extract a suitable permutation
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Experimental evaluation of preprocessing algorithms for constraint satisfaction problems
This paper presents an experimental evaluation of two orthogonal schemes for preprocessing constraint satisfaction problems (CSPs). The first of these schemes involves a class of local consistency techniques that includes directional arc consistency, directional path consistency, and adaptive consistency. The other scheme concerns the prearrangement of variables in a linear order to facilitate an efficient search. In the first series of experiments, we evaluated the effect of each of the local consistency techniques on backtracking and its common enhancement, backjumping. Surprizingly, although adaptive consistency has the best worst-case complexity bounds, we have found that it exhibits the worst performance, unless the constraint graph was very sparse. Directional arc consistency (followed by either backjumping or backtracking) and backjumping (without any pre-processing) outperformed all other techniques; moreover, the former dominated the latter in computationally intensive situations. The second series of experiments suggests that maximum cardinality and minimum width arc the best pre-ordering (i.e., static ordering) strategies, while dynamic search rearrangement is superior to all the preorderings studied
Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning
Finding tight bounds on the optimal solution is a critical element of
practical solution methods for discrete optimization problems. In the last
decade, decision diagrams (DDs) have brought a new perspective on obtaining
upper and lower bounds that can be significantly better than classical bounding
mechanisms, such as linear relaxations. It is well known that the quality of
the bounds achieved through this flexible bounding method is highly reliant on
the ordering of variables chosen for building the diagram, and finding an
ordering that optimizes standard metrics is an NP-hard problem. In this paper,
we propose an innovative and generic approach based on deep reinforcement
learning for obtaining an ordering for tightening the bounds obtained with
relaxed and restricted DDs. We apply the approach to both the Maximum
Independent Set Problem and the Maximum Cut Problem. Experimental results on
synthetic instances show that the deep reinforcement learning approach, by
achieving tighter objective function bounds, generally outperforms ordering
methods commonly used in the literature when the distribution of instances is
known. To the best knowledge of the authors, this is the first paper to apply
machine learning to directly improve relaxation bounds obtained by
general-purpose bounding mechanisms for combinatorial optimization problems.Comment: Accepted and presented at AAAI'1
Hydrogen atom in phase space. The Kirkwood-Rihaczek representation
We present a phase-space representation of the hydrogen atom using the
Kirkwood-Rikaczek distribution function. This distribution allows us to obtain
analytical results, which is quite unique because an exact analytical form of
the Wigner functions corresponding to the atom states is not known. We show how
the Kirkwood-Rihaczek distribution reflects properties of the hydrogen atom
wave functions in position and momentum representations.Comment: 5 pages (and 5 figures
Dynamical interpretation of the wavefunction of the universe
In this paper, we study the physical meaning of the wavefunction of the
universe. With the continuity equation derived from the Wheeler-DeWitt (WDW)
equation in the minisuperspace model, we show that the quantity
for the universe is inversely proportional to the Hubble
parameter of the universe. Thus, represents the probability density
of the universe staying in the state during its evolution, which we call
the dynamical interpretation of the wavefunction of the universe. We
demonstrate that the dynamical interpretation can predict the evolution laws of
the universe in the classical limit as those given by the Friedmann equation.
Furthermore, we show that the value of the operator ordering factor in the
WDW equation can be determined to be
Iterative solutions to the steady state density matrix for optomechanical systems
We present a sparse matrix permutation from graph theory that gives stable
incomplete Lower-Upper (LU) preconditioners necessary for iterative solutions
to the steady state density matrix for quantum optomechanical systems. This
reordering is efficient, adding little overhead to the computation, and results
in a marked reduction in both memory and runtime requirements compared to other
solution methods, with performance gains increasing with system size. Either of
these benchmarks can be tuned via the preconditioner accuracy and solution
tolerance. This reordering optimizes the condition number of the approximate
inverse, and is the only method found to be stable at large Hilbert space
dimensions. This allows for steady state solutions to otherwise intractable
quantum optomechanical systems.Comment: 10 pages, 5 figure
Symmetry Breaking Constraints: Recent Results
Symmetry is an important problem in many combinatorial problems. One way of
dealing with symmetry is to add constraints that eliminate symmetric solutions.
We survey recent results in this area, focusing especially on two common and
useful cases: symmetry breaking constraints for row and column symmetry, and
symmetry breaking constraints for eliminating value symmetryComment: To appear in Proceedings of Twenty-Sixth Conference on Artificial
Intelligence (AAAI-12
Learning optimization models in the presence of unknown relations
In a sequential auction with multiple bidding agents, it is highly
challenging to determine the ordering of the items to sell in order to maximize
the revenue due to the fact that the autonomy and private information of the
agents heavily influence the outcome of the auction.
The main contribution of this paper is two-fold. First, we demonstrate how to
apply machine learning techniques to solve the optimal ordering problem in
sequential auctions. We learn regression models from historical auctions, which
are subsequently used to predict the expected value of orderings for new
auctions. Given the learned models, we propose two types of optimization
methods: a black-box best-first search approach, and a novel white-box approach
that maps learned models to integer linear programs (ILP) which can then be
solved by any ILP-solver. Although the studied auction design problem is hard,
our proposed optimization methods obtain good orderings with high revenues.
Our second main contribution is the insight that the internal structure of
regression models can be efficiently evaluated inside an ILP solver for
optimization purposes. To this end, we provide efficient encodings of
regression trees and linear regression models as ILP constraints. This new way
of using learned models for optimization is promising. As the experimental
results show, it significantly outperforms the black-box best-first search in
nearly all settings.Comment: 37 pages. Working pape
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