42 research outputs found
A 3/2-Approximation for the Metric Many-visits Path TSP
In the Many-visits Path TSP, we are given a set of cities along with
their pairwise distances (or cost) , and moreover each city comes
with an associated positive integer request .
The goal is to find a minimum-cost path, starting at city and ending at
city , that visits each city exactly times.
We present a -approximation algorithm for the metric Many-visits
Path TSP, that runs in time polynomial in and poly-logarithmic in the
requests .
Our algorithm can be seen as a far-reaching generalization of the
-approximation algorithm for Path TSP by Zenklusen (SODA 2019), which
answered a long-standing open problem by providing an efficient algorithm which
matches the approximation guarantee of Christofides' algorithm from 1976 for
metric TSP.
One of the key components of our approach is a polynomial-time algorithm to
compute a connected, degree bounded multigraph of minimum cost.
We tackle this problem by generalizing a fundamental result of Kir\'aly, Lau
and Singh (Combinatorica, 2012) on the Minimum Bounded Degree Matroid Basis
problem, and devise such an algorithm for general polymatroids, even allowing
element multiplicities.
Our result directly yields a -approximation to the metric
Many-visits TSP, as well as a -approximation for the problem of
scheduling classes of jobs with sequence-dependent setup times on a single
machine so as to minimize the makespan.Comment: arXiv admin note: text overlap with arXiv:1911.0989
A time- and space-optimal algorithm for the many-visits TSP
The many-visits traveling salesperson problem (MV-TSP) asks for an optimal
tour of cities that visits each city a prescribed number of
times. Travel costs may be asymmetric, and visiting a city twice in a row may
incur a non-zero cost. The MV-TSP problem finds applications in scheduling,
geometric approximation, and Hamiltonicity of certain graph families.
The fastest known algorithm for MV-TSP is due to Cosmadakis and Papadimitriou
(SICOMP, 1984). It runs in time and
requires space. An interesting feature of the
Cosmadakis-Papadimitriou algorithm is its \emph{logarithmic} dependence on the
total length of the tour, allowing the algorithm to handle
instances with very long tours. The \emph{superexponential} dependence on the
number of cities in both the time and space complexity, however, renders the
algorithm impractical for all but the narrowest range of this parameter.
In this paper we improve upon the Cosmadakis-Papadimitriou algorithm, giving
an MV-TSP algorithm that runs in time , i.e.\
\emph{single-exponential} in the number of cities, using \emph{polynomial}
space. Our algorithm is deterministic, and arguably both simpler and easier to
analyse than the original approach of Cosmadakis and Papadimitriou. It involves
an optimization over directed spanning trees and a recursive, centroid-based
decomposition of trees.Comment: Small fixes, journal versio
Degree-bounded generalized polymatroids and approximating the metric many-visits TSP
In the Bounded Degree Matroid Basis Problem, we are given a matroid and a
hypergraph on the same ground set, together with costs for the elements of that
set as well as lower and upper bounds and for
each hyperedge . The objective is to find a minimum-cost basis
such that for
each hyperedge . Kir\'aly et al. (Combinatorica, 2012) provided an
algorithm that finds a basis of cost at most the optimum value which violates
the lower and upper bounds by at most , where is the
maximum degree of the hypergraph. When only lower or only upper bounds are
present for each hyperedge, this additive error is decreased to .
We consider an extension of the matroid basis problem to generalized
polymatroids, or g-polymatroids, and additionally allow element multiplicities.
The Bounded Degree g-polymatroid Element Problem with Multiplicities takes as
input a g-polymatroid instead of a matroid, and besides the lower and
upper bounds, each hyperedge has element multiplicities
. Building on the approach of Kir\'aly et al., we provide an
algorithm for finding a solution of cost at most the optimum value, having the
same additive approximation guarantee.
As an application, we develop a -approximation for the metric
Many-Visits TSP, where the goal is to find a minimum-cost tour that visits each
city a positive number of times. Our approach combines our algorithm
for the Bounded Degree g-polymatroid Element Problem with Multiplicities with
the principle of Christofides' algorithm from 1976 for the (single-visit)
metric TSP, whose approximation guarantee it matches.Comment: 17 page
SciSports: Learning football kinematics through two-dimensional tracking data
SciSports is a Dutch startup company specializing in football analytics. This
paper describes a joint research effort with SciSports, during the Study Group
Mathematics with Industry 2018 at Eindhoven, the Netherlands. The main
challenge that we addressed was to automatically process empirical football
players' trajectories, in order to extract useful information from them. The
data provided to us was two-dimensional positional data during entire matches.
We developed methods based on Newtonian mechanics and the Kalman filter,
Generative Adversarial Nets and Variational Autoencoders. In addition, we
trained a discriminator network to recognize and discern different movement
patterns of players. The Kalman-filter approach yields an interpretable model,
in which a small number of player-dependent parameters can be fit; in theory
this could be used to distinguish among players. The
Generative-Adversarial-Nets approach appears promising in theory, and some
initial tests showed an improvement with respect to the baseline, but the
limits in time and computational power meant that we could not fully explore
it. We also trained a Discriminator network to distinguish between two players
based on their trajectories; after training, the network managed to distinguish
between some pairs of players, but not between others. After training, the
Variational Autoencoders generated trajectories that are difficult to
distinguish, visually, from the data. These experiments provide an indication
that deep generative models can learn the underlying structure and statistics
of football players' trajectories. This can serve as a starting point for
determining player qualities based on such trajectory data.Comment: This report was made for the Study Group Mathematics with Industry
201
EASY-APP : An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis
Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed.The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit-learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross-validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross-validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP).The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy-to-use web application in the Streamlit Python-based framework (http://easy-app.org/).The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model
Early prediction of acute necrotizing pancreatitis by artificial intelligence : a prospective cohort-analysis of 2387 cases
Pancreatic necrosis is a consistent prognostic factor in acute pancreatitis (AP). However, the clinical scores currently in use are either too complicated or require data that are unavailable on admission or lack sufficient predictive value. We therefore aimed to develop a tool to aid in necrosis prediction. The XGBoost machine learning algorithm processed data from 2387 patients with AP. The confidence of the model was estimated by a bootstrapping method and interpreted via the 10th and the 90th percentiles of the prediction scores. Shapley Additive exPlanations (SHAP) values were calculated to quantify the contribution of each variable provided. Finally, the model was implemented as an online application using the Streamlit Python-based framework. The XGBoost classifier provided an AUC value of 0.757. Glucose, C-reactive protein, alkaline phosphatase, gender and total white blood cell count have the most impact on prediction based on the SHAP values. The relationship between the size of the training dataset and model performance shows that prediction performance can be improved. This study combines necrosis prediction and artificial intelligence. The predictive potential of this model is comparable to the current clinical scoring systems and has several advantages over them