Constrained Dual-Level Bandit for Personalized Impression Regulation in Online Ranking Systems

2021 ◽  
Vol 16 (2) ◽  
pp. 1-23
Author(s):  
Zhao Li ◽  
Junshuai Song ◽  
Zehong Hu ◽  
Zhen Wang ◽  
Jun Gao
Keyword(s):  
Optimization Problem ◽  
Initial Conditions ◽  
Global Scale ◽  
Global Constraints ◽  
Economic Losses ◽  
Second Order Cone ◽  
Ranking Systems ◽  
Item Level ◽  
Shopping Experience ◽  
Regulation Method

Impression regulation plays an important role in various online ranking systems, e.g. , e-commerce ranking systems always need to achieve local commercial demands on some pre-labeled target items like fresh item cultivation and fraudulent item counteracting while maximizing its global revenue. However, local impression regulation may cause “butterfly effects” on the global scale, e.g. , in e-commerce, the price preference fluctuation in initial conditions (overpriced or underpriced items) may create a significantly different outcome, thus affecting shopping experience and bringing economic losses to platforms. To prevent “butterfly effects”, some researchers define their regulation objectives with global constraints, by using contextual bandit at the page-level that requires all items on one page sharing the same regulation action, which fails to conduct impression regulation on individual items. To address this problem, in this article, we propose a personalized impression regulation method that can directly makes regulation decisions for each user-item pair. Specifically, we model the regulation problem as a C onstrained D ual-level B andit (CDB) problem, where the local regulation action and reward signals are at the item-level while the global effect constraint on the platform impression can be calculated at the page-level only. To handle the asynchronous signals, we first expand the page-level constraint to the item-level and then derive the policy updating as a second-order cone optimization problem. Our CDB approaches the optimal policy by iteratively solving the optimization problem. Experiments are performed on both offline and online datasets, and the results, theoretically and empirically, demonstrate CDB outperforms state-of-the-art algorithms.

scholarly journals Disease control as an optimization problem

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257958
Author(s):  
Miguel Navascués ◽  
Costantino Budroni ◽  
Yelena Guryanova
Keyword(s):  
Disease Control ◽  
Stochastic Models ◽  
Optimization Problem ◽  
Initial Conditions ◽  
Optimization Theory ◽  
Model Parameters ◽  
Grid Search ◽  
Brute Force ◽  
Time Required ◽  
Policy Optimization

In the context of epidemiology, policies for disease control are often devised through a mixture of intuition and brute-force, whereby the set of logically conceivable policies is narrowed down to a small family described by a few parameters, following which linearization or grid search is used to identify the optimal policy within the set. This scheme runs the risk of leaving out more complex (and perhaps counter-intuitive) policies for disease control that could tackle the disease more efficiently. In this article, we use techniques from convex optimization theory and machine learning to conduct optimizations over disease policies described by hundreds of parameters. In contrast to past approaches for policy optimization based on control theory, our framework can deal with arbitrary uncertainties on the initial conditions and model parameters controlling the spread of the disease, and stochastic models. In addition, our methods allow for optimization over policies which remain constant over weekly periods, specified by either continuous or discrete (e.g.: lockdown on/off) government measures. We illustrate our approach by minimizing the total time required to eradicate COVID-19 within the Susceptible-Exposed-Infected-Recovered (SEIR) model proposed by Kissler et al. (March, 2020).

scholarly journals Limit analysis of microstructures based on homogenization theory and the element-free Galerkin method

2020 ◽  
Vol 42 (4) ◽  
pp. 415-426
Author(s):  
Canh V. Le ◽  
Phuc L. H. Ho
Keyword(s):  
Optimization Problem ◽  
Shape Function ◽  
Limit State ◽  
Great Accuracy ◽  
Homogenization Theory ◽  
Element Free Galerkin Method ◽  
Element Free Galerkin ◽  
Second Order Cone ◽  
Numerical Formulation ◽  
Element Free

This paper presents a novel numerical formulation of computational homogenization analysis of materials at limit state. The fluctuating displacement field are approximated using the Element-Free Galerkin (EFG) meshless method. The estimated yield surface of materials can be determined by handling the multiscale (macro-micro) transition. Taking advantage of high-order EFG shape function and the second-order cone programming, the resulting optimization problem can be solved rapidly with the great accuracy. Several benchmark examples will be investigated to demonstrate the computational efficiency of proposed method.

scholarly journals On the Sensitivity of Local Flexibility Markets to Forecast Error: A Bi-Level Optimization Approach

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1959
Author(s):  
Delaram Azari ◽  
Shahab Shariat Torbaghan ◽  
Hans Cappon ◽  
Karel J. Keesman ◽  
Madeleine Gibescu ◽  
...  
Keyword(s):  
Distribution System ◽  
Large Scale ◽  
Optimization Problem ◽  
Distribution Networks ◽  
Second Order ◽  
Optimization Approach ◽  
Second Order Cone ◽  
Local Flexibility ◽  
Level Problem ◽  
Relaxation Error

The large-scale integration of intermittent distributed energy resources has led to increased uncertainty in the planning and operation of distribution networks. The optimal flexibility dispatch is a recently introduced, power flow-based method that a distribution system operator can use to effectively determine the amount of flexibility it needs to procure from the controllable resources available on the demand side. However, the drawback of this method is that the optimal flexibility dispatch is inexact due to the relaxation error inherent in the second-order cone formulation. In this paper we propose a novel bi-level optimization problem, where the upper level problem seeks to minimize the relaxation error and the lower level solves the earlier introduced convex second-order cone optimal flexibility dispatch (SOC-OFD) problem. To make the problem tractable, we introduce an innovative reformulation to recast the bi-level problem as a non-linear, single level optimization problem which results in no loss of accuracy. We subsequently investigate the sensitivity of the optimal flexibility schedules and the locational flexibility prices with respect to uncertainty in load forecast and flexibility ranges of the demand response providers which are input parameters to the problem. The sensitivity analysis is performed based on the perturbed Karush–Kuhn–Tucker (KKT) conditions. We investigate the feasibility and scalability of the proposed method in three case studies of standardized 9-bus, 30-bus, and 300-bus test systems. Simulation results in terms of local flexibility prices are interpreted in economic terms and show the effectiveness of the proposed approach.

scholarly journals Global hotspots for the occurrence of compound events

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Nina N. Ridder ◽  
Andy J. Pitman ◽  
Seth Westra ◽  
Anna Ukkola ◽  
Hong X. Do ◽  
...  
Keyword(s):  
Western Europe ◽  
Climate Models ◽  
Global Scale ◽  
Reanalysis Data ◽  
Economic Losses ◽  
Multiple Hazards ◽  
Weather And Climate ◽  
Spatial Estimates ◽  
Compound Events ◽  
Climate Events

AbstractCompound events (CEs) are weather and climate events that result from multiple hazards or drivers with the potential to cause severe socio-economic impacts. Compared with isolated hazards, the multiple hazards/drivers associated with CEs can lead to higher economic losses and death tolls. Here, we provide the first analysis of multiple multivariate CEs potentially causing high-impact floods, droughts, and fires. Using observations and reanalysis data during 1980–2014, we analyse 27 hazard pairs and provide the first spatial estimates of their occurrences on the global scale. We identify hotspots of multivariate CEs including many socio-economically important regions such as North America, Russia and western Europe. We analyse the relative importance of different multivariate CEs in six continental regions to highlight CEs posing the highest risk. Our results provide initial guidance to assess the regional risk of CE events and an observationally-based dataset to aid evaluation of climate models for simulating multivariate CEs.

scholarly journals A method for estimating Hill function-based dynamic models of gene regulatory networks

2018 ◽  
Vol 5 (2) ◽  
pp. 171226 ◽  
Author(s):  
Faizan Ehsan Elahi ◽  
Ammar Hasan
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Optimization Problem ◽  
Dynamic Models ◽  
Initial Conditions ◽  
Computational Cost ◽  
Optimization Approach ◽  
Continuous Models ◽  
Gene Regulatory ◽  
The Cost

Gene regulatory networks (GRNs) are quite large and complex. To better understand and analyse GRNs, mathematical models are being employed. Different types of models, such as logical, continuous and stochastic models, can be used to describe GRNs. In this paper, we present a new approach to identify continuous models, because they are more suitable for large number of genes and quantitative analysis. One of the most promising techniques for identifying continuous models of GRNs is based on Hill functions and the generalized profiling method (GPM). The advantage of this approach is low computational cost and insensitivity to initial conditions. In the GPM, a constrained nonlinear optimization problem has to be solved that is usually underdetermined. In this paper, we propose a new optimization approach in which we reformulate the optimization problem such that constraints are embedded implicitly in the cost function. Moreover, we propose to split the unknown parameter in two sets based on the structure of Hill functions. These two sets are estimated separately to resolve the issue of the underdetermined problem. As a case study, we apply the proposed technique on the SOS response in Escherichia coli and compare the results with the existing literature.

Force identification under unknown initial conditions by using concomitant mapping matrix and sparse regularization

2020 ◽  
pp. 107754632094469
Author(s):  
Xijun Ye ◽  
Chudong Pan
Keyword(s):  
Optimization Problem ◽  
Initial Conditions ◽  
Sparse Regularization ◽  
Force Identification ◽  
Moving Force ◽  
Dynamic Forces ◽  
Force Reconstruction ◽  
Structural Responses ◽  
One Step ◽  
Regularization Strategy

Unknown initial conditions can affect the identified accuracy of dynamic forces. Direct measurement of initial conditions is relatively difficult. This study proposes a sparse regularization–based method for identifying forces considering influences of unknown initial conditions. The initial conditions are embedded in a classical governing equation of force identification. The key idea is to introduce a concept of concomitant mapping matrix for reasonably expressing the initial conditions. First, a dictionary is introduced for expanding the dynamic forces. Then, the concomitant mapping matrix is formulated by using free vibrating responses, which correspond to structural responses happening after the structure is subjected to each atom of the force dictionary. A sparse regularization strategy is applied for solving the ill-conditioned equation. After that, the problem of force identification is converted into an optimization problem, and it can be solved by using a one-step strategy. Numerical simulations are carried out for verifying the feasibility and effectiveness of the proposed method. Illustrated results clearly show the applicability and robustness of the proposed method for dealing with force reconstruction and moving force identification.

scholarly journals Using Biased-Randomized Algorithms for the Multi-Period Product Display Problem with Dynamic Attractiveness

Algorithms ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 34
Author(s):  
Mage Marmol ◽  
Leandro do C. Martins ◽  
Sara Hatami ◽  
Angel A. Juan ◽  
Vicenc Fernandez
Keyword(s):  
Randomized Algorithms ◽  
Optimization Problem ◽  
Critical Issue ◽  
Linear Solvers ◽  
Sales Revenue ◽  
Shopping Experience ◽  
Brick And Mortar ◽  
Potential Customers ◽  
Efficient Selection ◽  
Selection Of

From brick-and-mortar stores to omnichannel retail, the efficient selection of products to be displayed on store tables, advertising brochures, or online front pages has become a critical issue. One possible goal is to maximize the overall ‘attractiveness’ level of the displayed items, i.e., to enhance the shopping experience of our potential customers as a way to increase sales and revenue. With the goal of maximizing the total attractiveness value for the visiting customers over a multi-period time horizon, this paper studies how to configure an assortment of products to be included in limited display spaces, either physical or online. In order to define a realistic scenario, several constraints are considered for each period and display table: (i) the inclusion of both expensive and non-expensive products on the display tables; (ii) the diversification of product collections; and (iii) the achievement of a minimum profit margin. Moreover, the attractiveness level of each product is assumed to be dynamic, i.e., it is reduced if the product has been displayed in a previous period (loss of novelty) and vice versa. This generates dependencies across periods. Likewise, correlations across items are also considered to account for complementary or substitute products. In the case of brick-and-mortar stores, for instance, solving this rich multi-period product display problem enables them to provide an exciting experience to their customers. As a consequence, an increase in sales revenue should be expected. In order to deal with the underlying optimization problem, which contains a quadratic objective function in its simplest version and a non-smooth one in its complete version, two biased-randomized metaheuristic algorithms are proposed. A set of new instances has been generated to test our approach and compare its performance with that of non-linear solvers.

scholarly journals The INGV–CMCC Seasonal Prediction System: Improved Ocean Initial Conditions

2010 ◽  
Vol 138 (7) ◽  
pp. 2930-2952 ◽  
Author(s):  
Andrea Alessandri ◽  
Andrea Borrelli ◽  
Simona Masina ◽  
Annalisa Cherchi ◽  
Silvio Gualdi ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Seasonal Prediction ◽  
Global Scale ◽  
Tropical Pacific ◽  
Optimal Interpolation ◽  
Boreal Winter ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Climate Anomalies ◽  
The Impact

Abstract The development of the Istituto Nazionale di Geofisica e Vulcanologia (INGV)–Centro Euro-Mediterraneo per i Cambiamenti Climatici (CMCC) Seasonal Prediction System (SPS) is documented. In this SPS the ocean initial-conditions estimation includes a reduced-order optimal interpolation procedure for the assimilation of temperature and salinity profiles at the global scale. Nine-member ensemble forecasts have been produced for the period 1991–2003 for two starting dates per year in order to assess the impact of the subsurface assimilation in the ocean for initialization. Comparing the results with control simulations (i.e., without assimilation of subsurface profiles during ocean initialization), it is shown that the improved ocean initialization increases the skill in the prediction of tropical Pacific sea surface temperatures of the system for boreal winter forecasts. Considering the forecast of the 1997/98 El Niño, the data assimilation in the ocean initial conditions leads to a considerable improvement in the representation of its onset and development. The results presented in this paper indicate a better prediction of global-scale surface climate anomalies for the forecasts started in November, probably because of the improvement in the tropical Pacific. For boreal winter, significant increases in the capability of the system to discriminate above-normal and below-normal temperature anomalies are shown in both the tropics and extratropics.

scholarly journals Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling

2013 ◽  
Vol 13 (19) ◽  
pp. 9917-9937 ◽  
Author(s):  
R. Locatelli ◽  
P. Bousquet ◽  
F. Chevallier ◽  
A. Fortems-Cheney ◽  
S. Szopa ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
Initial Conditions ◽  
Spatial Scales ◽  
Global Scale ◽  
Methane Emissions ◽  
Inverse Modelling ◽  
Model Errors ◽  
Inverse Systems ◽  
The Impact

Abstract. A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr−1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr−1 in North America to 7 Tg yr−1 in Boreal Eurasia (from 23 to 48%, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of transport model errors in current inverse systems. Future inversions should include more accurately prescribed observation covariances matrices in order to limit the impact of transport model errors on estimated methane fluxes.

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