Searched for: subject%3A%22Approximation%255C+algorithms%22
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Tramontina, Gino (author)
Bidirectional Reflectance Distribution Functions, BRDFs, describe the reflectance of light on a ma-terial, and are widely used in computer graphics to render materials. Acquiring a full measured BRDF can be costly and time consuming, so this research aims to answer the question ”How can we approx-imate a full BRDF from a single slice (in-plane...
bachelor thesis 2023
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Driebergen, Tim (author)
The minimum vertex cover problem (MinVertexCover) is an important optimization problem in graph theory, with applications in numerous fields outside of mathematics. As MinVertexCover is an NP-hard problem, there currently exists no efficient algorithm to find an optimal solution on arbitrary graphs. We consider quantum optimization algorithms,...
master thesis 2023
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Dai, Pengcheng (author), Yu, Wenwu (author), Wang, He (author), Baldi, S. (author)
Actor-critic (AC) cooperative multiagent reinforcement learning (MARL) over directed graphs is studied in this article. The goal of the agents in MARL is to maximize the globally averaged return in a distributed way, i.e., each agent can only exchange information with its neighboring agents. AC methods proposed in the literature require the...
journal article 2023
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Cremers, Sho (author)
With the emergence of energy communities, where a number of prosumers (consumers with their own energy generation) invest in shared renewable generation capacity and battery storage, the issue of fair allocation of benefits and costs has become increasingly important. The Shapley value, a solution concept in cooperative game theory initially...
master thesis 2022
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Perrenet, Joris (author)
The goal of seismic imaging is to create a model of the subsurface from samples of a transmitted wavefield that is reflected in soil layers.<br/>The created model can be used to locate storage possibilities for CO2 or H2 or to find oil and gas reservoirs or other natural resources.<br/>Seismic imaging relies on the propagation of wavefields,...
bachelor thesis 2022
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Groenheide, Jeroen (author)
Multi-Level variants of classic optimisation problems are becoming more noteworthy as the complexity of real life applications increases. In this research we investigate the Multi-Level Bin Packing optimisation problem, which models, for example, global logistics and part manufacturing. We will look at the performance of solving Integer Linear...
bachelor thesis 2022
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Fabiani, Filippo (author), Tajeddini, Mohammad Amin (author), Kebriaei, Hamed (author), Grammatico, S. (author)
We propose a two-layer, semi-decentralized algorithm to compute a local solution to the Stackelberg equilibrium problem in aggregative games with coupling constraints. Specifically, we focus on a single-leader, multiple follower problem, and after equivalently recasting the Stackelberg game as a mathematical program with complementarity...
journal article 2022
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Ananduta, W. (author), Grammatico, S. (author)
We formulate the optimal flow problem in a multi-area integrated electrical and gas system as a mixed-integer optimization problem by approximating the non-linear gas flows with piece-wise affine functions, thus resulting in a set of mixed-integer linear constraints. For its solution, we propose a novel algorithm that consists in one stage for...
conference paper 2022
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Dutta, Shamak (author), Wilde, N. (author), Smith, Stephen L. (author)
We present a new mixed integer formulation for the discrete informative path planning problem in random fields. The objective is to compute a budget constrained path while collecting measurements whose linear estimate results in minimum error over a finite set of prediction locations. The problem is known to be NP-hard. However, we strive to...
conference paper 2022
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Holtgrefe, Niels (author)
In this thesis we analyse the class of <i>maximum coverage</i> problems. For all discussed problems, linear programs are formulated. Using the notion of <i>submodularity</i>, we prove that for the weighted version of the basic <i>Maximum Coverage</i> problem, where the weights differ per set, a polynomial-time greedy algorithm guarantees a (1 -...
bachelor thesis 2021
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te Wierik, Ruben (author)
Real-time sequence clustering is the problem of clustering an infinite stream of sequences in real time with limited memory. A variant of the k-medoids algorithm called <i>SeqClu </i>is the suggested approach, representing a cluster with <i>p </i>most representative sequences of the cluster, called prototypes, to solve the problem of maintaining...
bachelor thesis 2021
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Franci, B. (author), Grammatico, S. (author)
We consider the stochastic generalized Nash equilibrium problem (SGNEP) with expected-value cost functions. Inspired by Yi and Pavel (2019), we propose a distributed generalized Nash equilibrium seeking algorithm based on the preconditioned forward-backward operator splitting for SGNEPs, where, at each iteration, the expected value of the...
journal article 2021
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Delimpaltadakis, Giannis (author), Mazo, M. (author)
In this article, we propose a region-based self-triggered control (STC) scheme for nonlinear systems. The state space is partitioned into a finite number of regions, each of which is associated to a uniform interevent time. The controller, at each sampling time instant, checks to which region does the current state belong, and correspondingly...
journal article 2021
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Munk, J. (author), Kober, J. (author), Babuska, R. (author)
Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). One advantage of DNNs is that they can cope with large input dimensions. Instead of relying on feature engineering to lower the input dimension, DNNs can extract the features from raw observations. The drawback of this end-to-end learning is that it...
conference paper 2016
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Byrka, Jaroslaw (author), Li, S. (author), Rybicki, Bartosz (author)
We study the k-level uncapacitated facility location problem (k-level UFL) in which clients need to be connected with paths crossing open facilities of k types (levels). In this paper we first propose an approximation algorithm that for any constant k, in polynomial time, delivers solutions of cost at most α<sub>k</sub> times OPT, where α<sub...
journal article 2014
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Mulder, J. (author)
bachelor thesis 2011
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Feldman, A.B. (author)
Model-based diagnosis is an area of abductive inference that uses a system model, together with observations about system behavior, to isolate sets of faulty components (diagnoses) that explain the observed behavior, according to some minimality criterion. This thesis presents greedy approximation algorithms for three problems closely related to...
doctoral thesis 2010
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Byrka, J. (author), Aardal, K.I. (author)
We obtain a 1.5-approximation algorithm for the metric uncapacitated facility location (UFL) problem, which improves on the previously best known 1.52-approximation algorithm by Mahdian, Ye, and Zhang. Note that the approximability lower bound by Guha and Khuller is 1.463 . . . . An algorithm is a (?f ,?c)-approximation algorithm if the solution...
journal article 2010
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