4,631 research outputs found
Reflection methods for user-friendly submodular optimization
Recently, it has become evident that submodularity naturally captures widely
occurring concepts in machine learning, signal processing and computer vision.
Consequently, there is need for efficient optimization procedures for
submodular functions, especially for minimization problems. While general
submodular minimization is challenging, we propose a new method that exploits
existing decomposability of submodular functions. In contrast to previous
approaches, our method is neither approximate, nor impractical, nor does it
need any cumbersome parameter tuning. Moreover, it is easy to implement and
parallelize. A key component of our method is a formulation of the discrete
submodular minimization problem as a continuous best approximation problem that
is solved through a sequence of reflections, and its solution can be easily
thresholded to obtain an optimal discrete solution. This method solves both the
continuous and discrete formulations of the problem, and therefore has
applications in learning, inference, and reconstruction. In our experiments, we
illustrate the benefits of our method on two image segmentation tasks.Comment: Neural Information Processing Systems (NIPS), \'Etats-Unis (2013
Combinatorial Continuous Maximal Flows
Maximum flow (and minimum cut) algorithms have had a strong impact on
computer vision. In particular, graph cuts algorithms provide a mechanism for
the discrete optimization of an energy functional which has been used in a
variety of applications such as image segmentation, stereo, image stitching and
texture synthesis. Algorithms based on the classical formulation of max-flow
defined on a graph are known to exhibit metrication artefacts in the solution.
Therefore, a recent trend has been to instead employ a spatially continuous
maximum flow (or the dual min-cut problem) in these same applications to
produce solutions with no metrication errors. However, known fast continuous
max-flow algorithms have no stopping criteria or have not been proved to
converge. In this work, we revisit the continuous max-flow problem and show
that the analogous discrete formulation is different from the classical
max-flow problem. We then apply an appropriate combinatorial optimization
technique to this combinatorial continuous max-flow CCMF problem to find a
null-divergence solution that exhibits no metrication artefacts and may be
solved exactly by a fast, efficient algorithm with provable convergence.
Finally, by exhibiting the dual problem of our CCMF formulation, we clarify the
fact, already proved by Nozawa in the continuous setting, that the max-flow and
the total variation problems are not always equivalent.Comment: 26 page
Maximum flows and minimum cuts in the plane
A continuous maximum flow problem finds the largest t such that div v = t F(x, y) is possible with a capacity constraint ||(v[subscript 1], v[subscript 2])|| ≤ c(x, y). The dual problem finds a minimum cut ∂ S which is filled to capacity by the flow through it. This model problem has found increasing application in medical imaging, and the theory continues to develop (along with new algorithms). Remaining difficulties include explicit streamlines for the maximum flow, and constraints that are analogous to a directed graph. Keywords: Maximum flow; Minimum cut; Capacity constraint; Cheege
A node-capacitated Okamura-Seymour theorem
The classical Okamura-Seymour theorem states that for an edge-capacitated,
multi-commodity flow instance in which all terminals lie on a single face of a
planar graph, there exists a feasible concurrent flow if and only if the cut
conditions are satisfied. Simple examples show that a similar theorem is
impossible in the node-capacitated setting. Nevertheless, we prove that an
approximate flow/cut theorem does hold: For some universal c > 0, if the node
cut conditions are satisfied, then one can simultaneously route a c-fraction of
all the demands. This answers an open question of Chekuri and Kawarabayashi.
More generally, we show that this holds in the setting of multi-commodity
polymatroid networks introduced by Chekuri, et. al. Our approach employs a new
type of random metric embedding in order to round the convex programs
corresponding to these more general flow problems.Comment: 30 pages, 5 figure
Robust Flows over Time: Models and Complexity Results
We study dynamic network flows with uncertain input data under a robust
optimization perspective. In the dynamic maximum flow problem, the goal is to
maximize the flow reaching the sink within a given time horizon , while flow
requires a certain travel time to traverse an edge.
In our setting, we account for uncertain travel times of flow. We investigate
maximum flows over time under the assumption that at most travel times
may be prolonged simultaneously due to delay. We develop and study a
mathematical model for this problem. As the dynamic robust flow problem
generalizes the static version, it is NP-hard to compute an optimal flow.
However, our dynamic version is considerably more complex than the static
version. We show that it is NP-hard to verify feasibility of a given candidate
solution. Furthermore, we investigate temporally repeated flows and show that
in contrast to the non-robust case (that is, without uncertainties) they no
longer provide optimal solutions for the robust problem, but rather yield a
worst case optimality gap of at least . We finally show that the optimality
gap is at most , where and are newly introduced
instance characteristics and provide a matching lower bound instance with
optimality gap and . The results obtained in
this paper yield a first step towards understanding robust dynamic flow
problems with uncertain travel times
Submodular relaxation for inference in Markov random fields
In this paper we address the problem of finding the most probable state of a
discrete Markov random field (MRF), also known as the MRF energy minimization
problem. The task is known to be NP-hard in general and its practical
importance motivates numerous approximate algorithms. We propose a submodular
relaxation approach (SMR) based on a Lagrangian relaxation of the initial
problem. Unlike the dual decomposition approach of Komodakis et al., 2011 SMR
does not decompose the graph structure of the initial problem but constructs a
submodular energy that is minimized within the Lagrangian relaxation. Our
approach is applicable to both pairwise and high-order MRFs and allows to take
into account global potentials of certain types. We study theoretical
properties of the proposed approach and evaluate it experimentally.Comment: This paper is accepted for publication in IEEE Transactions on
Pattern Analysis and Machine Intelligenc
Barrier Frank-Wolfe for Marginal Inference
We introduce a globally-convergent algorithm for optimizing the
tree-reweighted (TRW) variational objective over the marginal polytope. The
algorithm is based on the conditional gradient method (Frank-Wolfe) and moves
pseudomarginals within the marginal polytope through repeated maximum a
posteriori (MAP) calls. This modular structure enables us to leverage black-box
MAP solvers (both exact and approximate) for variational inference, and obtains
more accurate results than tree-reweighted algorithms that optimize over the
local consistency relaxation. Theoretically, we bound the sub-optimality for
the proposed algorithm despite the TRW objective having unbounded gradients at
the boundary of the marginal polytope. Empirically, we demonstrate the
increased quality of results found by tightening the relaxation over the
marginal polytope as well as the spanning tree polytope on synthetic and
real-world instances.Comment: 25 pages, 12 figures, To appear in Neural Information Processing
Systems (NIPS) 2015, Corrected reference and cleaned up bibliograph
Inexactness of SDP Relaxation and Valid Inequalities for Optimal Power Flow
It has been recently proven that the semidefinite programming (SDP)
relaxation of the optimal power flow problem over radial networks is exact
under technical conditions such as not including generation lower bounds or
allowing load over-satisfaction. In this paper, we investigate the situation
where generation lower bounds are present. We show that even for a two-bus
one-generator system, the SDP relaxation can have all possible approximation
outcomes, that is (1) SDP relaxation may be exact or (2) SDP relaxation may be
inexact or (3) SDP relaxation may be feasible while the OPF instance may be
infeasible. We provide a complete characterization of when these three
approximation outcomes occur and an analytical expression of the resulting
optimality gap for this two-bus system. In order to facilitate further
research, we design a library of instances over radial networks in which the
SDP relaxation has positive optimality gap. Finally, we propose valid
inequalities and variable bound tightening techniques that significantly
improve the computational performance of a global optimization solver. Our work
demonstrates the need of developing efficient global optimization methods for
the solution of OPF even in the simple but fundamental case of radial networks
Robust network design under polyhedral traffic uncertainty
Ankara : The Department of Industrial Engineering and The Institute of Engineering and Science of Bilkent Univ., 2007.Thesis (Ph.D.) -- Bilkent University, 2007.Includes bibliographical references leaves 160-166.In this thesis, we study the design of networks robust to changes in demand
estimates. We consider the case where the set of feasible demands is defined by
an arbitrary polyhedron. Our motivation is to determine link capacity or routing
configurations, which remain feasible for any realization in the corresponding
demand polyhedron. We consider three well-known problems under polyhedral
demand uncertainty all of which are posed as semi-infinite mixed integer programming
problems. We develop explicit, compact formulations for all three problems
as well as alternative formulations and exact solution methods.
The first problem arises in the Virtual Private Network (VPN) design field.
We present compact linear mixed-integer programming formulations for the problem
with the classical hose traffic model and for a new, less conservative, robust
variant relying on accessible traffic statistics. Although we can solve these formulations
for medium-to-large instances in reasonable times using off-the-shelf MIP
solvers, we develop a combined branch-and-price and cutting plane algorithm to
handle larger instances. We also provide an extensive discussion of our numerical
results.
Next, we study the Open Shortest Path First (OSPF) routing enhanced with
traffic engineering tools under general demand uncertainty with the motivation to
discuss if OSPF could be made comparable to the general unconstrained routing
(MPLS) when it is provided with a less restrictive operating environment. To
the best of our knowledge, these two routing mechanisms are compared for the
first time under such a general setting. We provide compact formulations for
both routing types and show that MPLS routing for polyhedral demands can
be computed in polynomial time. Moreover, we present a specialized branchand-price
algorithm strengthened with the inclusion of cuts as an exact solution tool. Subsequently, we compare the new and more flexible OSPF routing with
MPLS as well as the traditional OSPF on several network instances. We observe
that the management tools we use in OSPF make it significantly better than the
generic OSPF. Moreover, we show that OSPF performance can get closer to that
of MPLS in some cases.
Finally, we consider the Network Loading Problem (NLP) under a polyhedral
uncertainty description of traffic demands. After giving a compact multicommodity
formulation of the problem, we prove an unexpected decomposition
property obtained from projecting out the flow variables, considerably simplifying
the resulting polyhedral analysis and computations by doing away with metric inequalities,
an attendant feature of most successful algorithms on NLP. Under the
hose model of feasible demands, we study the polyhedral aspects of NLP, used as
the basis of an efficient branch-and-cut algorithm supported by a simple heuristic
for generating upper bounds. We provide the results of extensive computational
experiments on well-known network design instances.Altın, AyşegülPh.D
Global Optimisation for Energy System
The goal of global optimisation is to find globally optimal solutions, avoiding local optima and other stationary points. The aim of this thesis is to provide more efficient global optimisation tools for energy systems planning and operation. Due to the ongoing increasing of complexity and decentralisation of power systems, the use of advanced mathematical techniques that produce reliable solutions becomes necessary. The task of developing such methods is complicated by the fact that most energy-related problems are nonconvex due to the nonlinear Alternating Current Power Flow equations and the existence of discrete elements. In some cases, the computational challenges arising from the presence of non-convexities can be tackled by relaxing the definition of convexity and identifying classes of problems that can be solved to global optimality by polynomial time algorithms. One such property is known as invexity and is defined by every stationary point of a problem being a global optimum. This thesis investigates how the relation between the objective function and the structure of the feasible set is connected to invexity and presents necessary conditions for invexity in the general case and necessary and sufficient conditions for problems with two degrees of freedom. However, nonconvex problems often do not possess any provable convenient properties, and specialised methods are necessary for providing global optimality guarantees. A widely used technique is solving convex relaxations in order to find a bound on the optimal solution. Semidefinite Programming relaxations can provide good quality bounds, but they suffer from a lack of scalability. We tackle this issue by proposing an algorithm that combines decomposition and linearisation approaches. In addition to continuous non-convexities, many problems in Energy Systems model discrete decisions and are expressed as mixed-integer nonlinear programs (MINLPs). The formulation of a MINLP is of significant importance since it affects the quality of dual bounds. In this thesis we investigate algebraic characterisations of on/off constraints and develop a strengthened version of the Quadratic Convex relaxation of the Optimal Transmission Switching problem. All presented methods were implemented in mathematical modelling and optimisation frameworks PowerTools and Gravity
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