# Dag HauglandUniversity of Bergen | UiB · Department of Informatics

Dag Haugland

Dr.Scient.

## About

74

Publications

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Introduction

## Publications

Publications (74)

Given a graph $G=(V,E)$ and a set $C$ of unordered pairs of edges regarded as being in conflict, a stable spanning tree in $G$ is a set of edges $T$ inducing a spanning tree in $G$, such that for each $\left\lbrace e_i, e_j \right\rbrace \in C$, at most one of the edges $e_i$ and $e_j$ is in $T$. The existing work on Lagrangean algorithms to the NP...

Given a graph and a subset of its nodes, referred to as source nodes, the minimum broadcast problem asks for the minimum number of steps in which a signal can be transmitted from the sources to all other nodes in the graph. In each step, the sources and the nodes that already have received the signal can forward it to at most one of their neighbor...

Given an undirected graph G=(V,E) and a positive integer k∈1,…,|V|, we initiate the combinatorial study of stable sets of cardinality exactly k in G. Our aim is to instigate the polyhedral investigation of the convex hull of fixed cardinality stable sets, inspired by the rich theory on the classical structure of stable sets. We introduce a large cl...

Given an undirected graph G = (V, E) and an integer k∈1,…,|V|, we initiate the combinatorial study of stable sets of cardinality exactly k in G. Our aim is to instigate the polyhedral investigation of the convex hull of fixed cardinality stable sets, and we begin by introducing a large class of valid inequalities to the natural integer programming...

A novel approach for optimizing reliable cable layouts in offshore wind farms is presented. While optimization models traditionally are designed to suggest acyclic cable routes, those developed in this work recognize that cyclic layouts reduce the consequences of cable failures. The models under study take into account that cables cannot cross each...

We study the shared multicast tree (SMT) problem in wireless networks. To support a multicast session between a set of network nodes, SMT aims to establish a wireless connection between them, such that the total energy consumption is minimized. All destinations of the multicast message must be connected, while non-destinations are optional nodes th...

Maintenance costs account for a large part of the total cost of an offshore wind farm. Several models have been presented in the literature to optimize the fleet composition of the required vessels to support maintenance tasks. We provide a mixed integer linear programming (MILP) description of such a model, where on the higher level, the fleet com...

In this article, an integer linear programming model for cost minimization of cable layouts in offshore wind farms is presented. All turbines must be connected to power substations by cables. Up to a given number, turbines may be connected along a joint cable in a series circuit, and cable branching at turbine locations is possible. No two cables a...

Maintenance provides a large part of the cost of an offshore wind farm. Several models have been presented in literature to optimize the fleet composition of the required vessels. A drawback such models is that they are based on perfect information on weather and incidences to schedule for the coming year. Our research question is what will happen...

This work describes logistical planning of offshore wind farm (OWF) installation through linear programming. A mixed integer linear programming (MILP) model is developed to analyze cost-effective port and vessel strategies for offshore installation operations. The model seeks to minimize total costs through strategic decisions, that is decisions on...

The offshore wind energy industry is expected to continue its growth tendency in the near future. The European Wind Energy Association expects in its Central Scenario by 2030 a total installed capacity of 66 GW of offshore wind in the UE. Offshore wind farms (OWFs) are large scale infrastructures, requiring maintenance fleets to perform operations...

We study the problem of selecting a restricted number of shares included in a stock market index, such that the portfolio resembles the index as closely as possible. To measure the difference between the portfolio and the index, referred to as the tracking error, we use a quadratic function with the covariance matrix of the index returns as coeffic...

We present a discrete optimisation model that chooses an optimal fleet of vessels to support maintenance operations at Offshore Wind Farms (OFWs). The model is presented as a bi-level problem. On the first (tactical) level, decisions are made on the fleet composition for a certain time horizon. On the second (operational) level, the fleet is used t...

The computational challenge offered by many traditional network flow models is modest, and large-scale instances can be solved fast. When the composition of the flow is part of the model, the required computation time may increase substantially. This is in particular true for the pooling problem, where the relative content of certain flow component...

For oil and gas fields in production in the North Sea, a key task is to maximize the profit made by recovery and processing of oil/gas reserves. A main source of increased hydrocarbon off-take is through wells, which can be either producers or injectors (gas or water). The choice of technology for field development depends upon key parameters like...

The computational challenge offered by most traditional network flow models is modest, and large scale instances can be solved fast. The challenge becomes more serious if the composition of the flow has to be taken into account. This is in particular true for the pooling problem, where the relative content of certain flow components is restricted....

An integer programming model for minimizing the cabling costs of offshore wind farms which allows for branching of the cables is developed. Model features include upper bounds on the number of cable branches made at any wind turbine, upper bounds on the cable loads, and exclusion of crossing cable segments. The resulting model resembles the capacit...

The pooling problem is an extension of the minimum cost flow problem defined on a directed graph with three layers of nodes, where quality constraints are introduced at each terminal node. Flow entering the network at the source nodes has a given quality, at the internal nodes (pools) the entering flow is blended, and then sent to the terminal node...

Pooling and blending are important operations in petrochemical and agricultural industries with high potential economic value. For instance, transporting the natural gas from the production sources to the exit terminals is a complex process where the end products in the terminals consist of a blend of natural gas from different sources. Constraints...

The pooling problem is a well-studied global optimization problem with applications in oil refining and petrochemical industry. Despite the strong NP-hardness of the problem, which is proved formally in this paper, most instances from the literature have recently been solved efficiently by use of strong formulations. The main contribution from this...

The pooling problem is an extension of the minimum cost network flow problem where the composition of the flow depends on the sources from which it originates. At each source, the composition is known. In all other nodes, the proportion of any component is given as a weighted average of its proportions in entering flow streams. The weights in this...

We consider the problem of constructing a shared broadcast tree (SBT) in wireless networks, such that the total power required for supporting broadcast initiated by all source nodes is minimal. In the well-studied minimum-energy broadcast (MEB) problem, the optimal tree varies by source. In contrast, SBT is source-independent, thus substantially re...

In many transportation systems, the shipment quantities are subject to minimum lot sizes in addition to regular capacity constraints. This means that either the quantity must be zero, or it must be between the two bounds. In this work, we prove that the maximum flow problem with minimum lot-size constraints on the arcs is strongly NP-hard, and we e...

In this paper, the problem of flow maximization in pipeline systems for transmission of natural gas is addressed. We extend previously suggested models by incorporating the variation in pipeline flow capacities with gas specific gravity and compressibility. Flow capacities are modeled as functions of pressure, compressibility and specific gravity b...

This paper introduces the probabilistic dial-a-ride problem, and describes an efficient request-relocation neighborhood evaluation
procedure for the problem. The running time of the procedure is O(n5){\mathcal{O}(n^5)} , compared to O(n6){\mathcal{O}(n^6)} for a straightforward approach. For solving the problem we embed the suggested evaluation pro...

In this paper, the problem of computing optimal transportation plans for natural gas by means of compressor stations in pipeline networks is addressed. The non-linear (non-convex) mathematical model considers two types of continuous decision variables: mass flow rate along each arc, and gas pressure level at each node. The problem arises due to the...

The pooling problem is an important global optimization problem which is encountered in many industrial settings. It is traditionally modeled as a bilinear, nonconvex optimization problem, and solved by branch-and-bound algorithms where the subproblems are convex. In some industrial applications, for instance in pipeline transportation of natural g...

In many transportation systems, the shipment quantities are subject to minimum lot sizes in addition to regular capacity constraints. That is, either the quantity must be zero, or it must be between the two bounds. In this work, we consider a directed graph, where a minimum lot size and a flow capacity are defined for each arc, and study the proble...

This chapter examines two types of optimization problems of minimizing the total transmission power required to satisfy some
connectivity requirement for a group of nodes in ad hoc wireless networks. The first problem type is broadcast and multicast
of messages from a source node to the rest of the group. The second problem type, also known as rang...

Hunsaker and Savelsbergh have proposed an algorithm for testing feasibility of a route in the solution to the dial-a-ride
problem. The constraints that are checked are load capacity constraints, time windows, ride time bounds and wait time bounds.
The algorithm has linear running time. By virtue of a simple example, we show in this work that their...

Network flow models, where the flow quality is given at the supply nodes and quality constraints are defined at the demand
nodes, appear frequently when optimizing refinery operations, pipeline gas transportation, and other energy related operations.
Tracing the quality of the flow from the supplier to the market implies that the quality must be up...

Quickly finding low-energy multicast routings is vital for a wireless system’s energy efficiency. Therefore, key aspects of heuristics for the minimum energy multicast problem (MEMP) are low time complexity (measured in the numbers |V| and |A| of networking devices and their possible power assignments, respectively) and low deviation from the optim...

We study power requirements of network coding on a network with broadcast links, for a given multicast session. A common approach to network coding is to extract a subgraph from the network, to which network coding is applied. In order to facilitate network encoding, it is possible to impose the constraint that this subgraph is acyclic. However, in...

The search for optimal multicast subgraphs for network coding is considered. We assume unit link capacities and binary flow rates. In the first version of the problem, there is no constraint on the acyclicity of the subgraphs, whereas such constraints are imposed in the second version. These problems are known to be NP-hard. We provide heuristics t...

In this paper, we address the problem of computing optimal transportation plans of natural gas by means of compressor stations in pipeline networks. This non-linear (non-convex) problem takes into account two types of continuous decision variables: mass flow rate through each arc, and gas pressure level at each node. Compressors consume fuel at rat...

In this paper we apply an existing simple optimization algorithm to the pooling problem, which is a hard global optimization problem occurring among other places in the petroleum industry. We propose a technique for generating initial solutions and show that the global optimum is found on many standard problems using relatively few starting points....

The Broadcast Incremental Power (BIP) algorithm is the most frequently cited method for the minimum energy broadcast routing problem. A recent survey concluded that BIP has O(|V|3) time complexity, and that its approximation ratio is at least 4.33. We strengthen these results to O(|V|2) and 4.598, respectively.

Local search methods are often used to reduce the power consumption of broadcast routing in wireless networks. For a classic method, sweep, the best available time complexity result is O(V4). We present an O(V2)-time method, which exhaustively removes unnecessary transmissions yielding a solution comparable to that of sweep.

SUMMARY The Hamiltonian Monte Carlo (HMC) algorithm is a Markov Chain Monte Carlo (MCMC) technique, which combines the advantages of Hamiltonian dynamics methods and Metropolis Monte Carlo approach, to sample from complex distributions. The HMC algorithm incorporates gradient information in the dynamic trajectories and thus suppresses the random wa...

The Hamiltonian Monte Carlo (HMC) algorithm is a Markov Chain Monte Carlo (MCMC) technique, which combines the advantages of Hamiltonian dynamics methods and Metropolis Monte Carlo approach, to sample from complex distributions. The HMC algorithm incorporates gradient information in the dynamic trajectories and thus suppresses the random walk natur...

A multicast session in a wireless ad hoc network concerns routing messages from a source to a set of destination devices. Transmitting messages consumes energy at the source and intermediate devices of the session. Since a battery is the only energy source in many applications of wireless ad hoc networks, energy efficiency is an important performan...

Both integer programming models and heuristic algorithms have been proposed for finding minimum-energy broadcast and multicast trees in wireless ad hoc networks. Among heuristic algorithms, the broadcast/multicast incremental power (BIP/MIP) algorithm is most known. The theoretical performance of BIP/MIP has been quantified in several studies. To a...

A new type of attack on secure network coding is introduced in this paper. In this model, network nodes, which handle the
traffic from the source node to sink nodes are potentially viewed to be corruptible. We study the maximum security capacity
for this problem for a single- source single-sink scenario, and we generalize our study for multicast wi...

Ability to find a low-energy broadcast routing quickly is vital to a wireless system’s energy efficiency. Directional antennae
save power by concentrating the transmission towards the intended destinations. A routing is given by assigning a transmission
power, angle, and direction to every networking unit, and the problem of finding such a power sa...

This paper considers the problem of designing districts for vehicle routing problems with stochastic demands. In particular, demands are assumed to be uncertain at the time when the districts are made, and these are revealed only after the districting decisions are determined. Tabu search and multistart heuristics for this stochastic districting pr...

In this work we study a model for regularity analysis of gas production and transportation systems. The model is based on Monte Carlo simulation of discrete events such as equipment breakdown and repair, which makes it necessary to handle a number of possible system states. Each state is characterized not only by the set of currently operating syst...

We consider the problem of finding a linear combination of at most t out of K column vectors in a matrix, such that a target vector is approximated as closely as possible. The motivation of the model
is to find a lower-dimensional representation of a given signal vector (target) while minimizing loss of accuracy. We point
out the computational intr...

The routing of a fleet of vehicles to service a set of customers is important in the field of distribution. Vehicle routing problems (VRP) arise in many real-life applications within transportation and logistics. In this paper, we study a variant of the general VRP, VRP with time windows and split deliveries (VRPTWSD). Time constrained routing is r...

We consider the problem of finding a linear combination of at most t out of K column vectors in a matrix, such that a target vector is approximated as closely as possible. The motivation of the model is to find a lower-dimensional representation of a given signal vector (target) while minimizing loss of accuracy. We point out the computational intr...

We consider the problem of finding a linear combination of at most t out of K column vectors in a matrix, such that a target vector is approximated as closely as possible. The motivation of the model is to find a lower-dimensional representation of a given signal vector (target) while minimizing loss of accuracy. We point out the computational intr...

Storage and transmission of ElectroCardioGram (ECG) signals involve large amounts of data. Techniques for lossy data compression are useful in reducing the needed storage space or transmission time for such signals. Several dedicated time domain methods exist for this purpose, including the AZTEC, CORTES, Turning Point, and FAN algorithms. This art...

In this paper we present a time domain signal compression scheme based on the coding of line segments which are used to approximate the signal. These segments are fit in a way that is optimal given the number of retained signal samples. Although the approach is useful for many types of signals, we focus in this paper on compression of ElectroCardio...

We present a signal compression scheme based on coding linear segments approximating the signal. Although the approach is useful for many types of signals, we focus in this paper on compression of electrocardiogram (EGG) signals. The ECG signal compression has traditionally been tackled by heuristic approaches. However, it has been demonstrated tha...

Compression of image contours is an important problem in many contexts. An example is object oriented video coding, where eOEcient encoding of shape information of arbitrarily shaped objects is a major problem. This paper presents a method for compressing contours by extracting representative points from the original curve. By formulating the point...

Compression of digital electrocardiogram (ECG) signals has
traditionally been tackled by heuristical approaches. It has been
demonstrated that exact optimization algorithms outclass these
heuristical approaches by a wide margin with respect to the
reconstruction error. As opposed to traditional time-domain algorithms,
where some heuristic is used t...

Traditionally, compression of digital ElectroCardioGram (ECG) signals has been tackled by heuristical approaches. However, it has recently been demonstrated that exact optimization algorithms perform much better with respect to reconstruction error. Time domain compression algorithms are based on the idea of extracting representative signal samples...

Compression of digital ElectroCardioGram (ECG) signals has traditionally been tackled by heuristical approaches. Recently, it has been demonstrated [1] that exact optimization algorithms outclass these heuristical approaches by a wide margin with respect to reconstruction error. As opposed to traditional time-domain algorithms, where some heuristic...

The use of exact optimisation algorithms for compressing digital electrocardiograms (ECGs) is demonstrated. As opposed to traditional time-domain methods, which use heuristics to select a small subset of representative signal samples, the problem of selecting the subset is formulated in rigorous mathematical terms. This approach makes it possible t...

The problem of compressing digital electrocardiograms (ECG) has traditionally been tackled by heuristic approaches. Recently, it has been demonstrated how techniques based on graph theory can be applied in order to yield optimal compressions. Unlike the conventional methods, the graph algorithms guarantee the minimal reproduction error given some r...

Storage and transmission of signals tend to be resource demanding when the data are processed in their original size. This is not confined to multi-dimensional signals such as images, but is also most relevant in the one-dimensional case considered in this work. Compressing the data in such a way that close reconstructions can be found, is therefor...

This paper presents a practical implementation of an optimal time
domain electrocardiogram (ECG) compression scheme based on graph theory.
For a given set of signal samples, the optimal scheme produces the
minimum set of samples required to satisfy a preset bound on the RMS
error of the reconstructed signal. The problem is solved by a cubic
dynamic...

In this paper we present an algorithm for the pooling problem in refinery optimization based on a bilinear programming approach. The pooling problem occurs frequently in process optimization problems, especially refinery planning models. The main difficulty is that pooling causes an inherent nonlinearity in the otherwise linear models. We shall def...

Mathematical programming models for an early evaluation of a petroleum field have previously been developed. Among these is a mixed integer programming model that incorporates a reservoir simulation and an economic evaluation model. This model is formulated to answer questions related to where wells should be drilled when the goal is to get the bes...

In 1984, Wets presented a method for solving many linear programs that differ only in the righthand side. The idea is to build up a search tree, where each node corresponds to a dual feasible basis for the linear program, and each arc to a dual pivot step. Nodes are added to the search tree as they are needed, and the linear programs are solved by...

This paper presents some models for an early evaluation of a petroleum field. Based on crude assumptions about a reservoir, our models suggest decisions concerning platform capacity, drilling programme and production. We start out with a simple production planning model using linear programming. By mixed integer programming techniques the model is...

The Subspace Selection Problem (SSP) amounts to selecting t out of n given vectors of dimension m, such that they span a subspace in which a given target
b Î Âmb\in\Re^m
has a closest possible approximation. This model has numerous applications in e.g. signal compression and statistical regression.
It is well known that the problem is NP-hard. Bas...

We consider the problem of determining a two-dimensional surface in a three-dimensional space. A proposed surface, represented by a discrete point set is already given. The proposal may however not correspond to the true surface. Associated with each proposed point, a set of admissible points is provided. The problem is to select one admissible poi...