scholarly journals Staying Ahead of the Game: Adaptive Robust Optimization for Dynamic Allocation of Threat Screening Resources

2017 ◽  
Author(s):  
Sara Marie Mc Carthy ◽  
Phebe Vayanos ◽  
Milind Tambe
Keyword(s):  
Robust Optimization ◽  
Decision Rules ◽  
Dynamic Allocation ◽  
Arrival Times ◽  
X Ray ◽  
Solution Approach ◽  
X Ray Imaging ◽  
Solution Methods ◽  
Linear Decision Rules ◽  
And Performance

We consider the problem of dynamically allocating screening resources of different efficacies (e.g., magnetic or X-ray imaging) at checkpoints (e.g., at airports or ports) to successfully avert an attack by one of the screenees. Previously, the Threat Screening Game model was introduced to address this problem under the assumption that screenee arrival times are perfectly known. In reality, arrival times are uncertain, which severely impedes the implementability and performance of this approach. We thus propose a novel framework for dynamic allocation of threat screening resources that explicitly accounts for uncertainty in the screenee arrival times. We model the problem as a multistage robust optimization problem and propose a tractable solution approach using compact linear decision rules combined with robust reformulation and constraint randomization. We perform extensive numerical experiments which showcase that our approach outperforms (a) exact solution methods in terms of tractability, while incurring only a very minor loss in optimality, and (b) methods that ignore uncertainty in terms of both feasibility and optimality.

Robust Optimization for Models with Uncertain Second-Order Cone and Semidefinite Programming Constraints

2021 ◽  
Author(s):  
Jianzhe Zhen ◽  
Frans J. C. T. de Ruiter ◽  
Ernst Roos ◽  
Dick den Hertog
Keyword(s):  
Robust Optimization ◽  
Semidefinite Programming ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Decision Rules ◽  
Second Order ◽  
Minimum Volume ◽  
Second Order Cone ◽  
Linear Decision Rules ◽  
Effectiveness And Efficiency

In this paper, we consider uncertain second-order cone (SOC) and semidefinite programming (SDP) constraints with polyhedral uncertainty, which are in general computationally intractable. We propose to reformulate an uncertain SOC or SDP constraint as a set of adjustable robust linear optimization constraints with an ellipsoidal or semidefinite representable uncertainty set, respectively. The resulting adjustable problem can then (approximately) be solved by using adjustable robust linear optimization techniques. For example, we show that if linear decision rules are used, then the final robust counterpart consists of SOC or SDP constraints, respectively, which have the same computational complexity as the nominal version of the original constraints. We propose an efficient method to obtain good lower bounds. Moreover, we extend our approach to other classes of robust optimization problems, such as nonlinear problems that contain wait-and-see variables, linear problems that contain bilinear uncertainty, and general conic constraints. Numerically, we apply our approach to reformulate the problem on finding the minimum volume circumscribing ellipsoid of a polytope and solve the resulting reformulation with linear and quadratic decision rules as well as Fourier-Motzkin elimination. We demonstrate the effectiveness and efficiency of the proposed approach by comparing it with the state-of-the-art copositive approach. Moreover, we apply the proposed approach to a robust regression problem and a robust sensor network problem and use linear decision rules to solve the resulting adjustable robust linear optimization problems, which solve the problem to (near) optimality. Summary of Contribution: Computing robust solutions for nonlinear optimization problems with uncertain second-order cone and semidefinite programming constraints are of much interest in real-life applications, yet they are in general computationally intractable. This paper proposes a computationally tractable approximation for such problems. Extensive computational experiments on (i) computing the minimum volume circumscribing ellipsoid of a polytope, (ii) robust regressions, and (iii) robust sensor networks are conducted to demonstrate the effectiveness and efficiency of the proposed approach.

scholarly journals Calibration and Performance of the REgolith X-Ray Imaging Spectrometer (REXIS) Aboard NASA’s OSIRIS-REx Mission to Bennu

2021 ◽  
Vol 217 (8) ◽  
Author(s):  
Jaesub Hong ◽  
Richard P. Binzel ◽  
Branden Allen ◽  
David Guevel ◽  
Jonathan Grindlay ◽  
...  
Keyword(s):  
Imaging Spectrometer ◽  
X Ray ◽  
X Ray Imaging ◽  
And Performance

Primal and dual linear decision rules in stochastic and robust optimization

2009 ◽  
Vol 130 (1) ◽  
pp. 177-209 ◽  
Author(s):  
Daniel Kuhn ◽  
Wolfram Wiesemann ◽  
Angelos Georghiou
Keyword(s):  
Robust Optimization ◽  
Decision Rules ◽  
Linear Decision Rules ◽  
Primal And Dual

scholarly journals A phase-retrieval toolbox for X-ray holography and tomography

2020 ◽  
Vol 27 (3) ◽  
pp. 852-859 ◽  
Author(s):  
Leon M. Lohse ◽  
Anna-Lena Robisch ◽  
Mareike Töpperwien ◽  
Simon Maretzke ◽  
Martin Krenkel ◽  
...  
Keyword(s):  
Phase Contrast ◽  
Phase Retrieval ◽  
Performance Test ◽  
Limiting Factor ◽  
Full Field ◽  
X Ray ◽  
X Ray Imaging ◽  
Wide Range ◽  
Experimental Parameters ◽  
And Performance

Propagation-based phase-contrast X-ray imaging is by now a well established imaging technique, which – as a full-field technique – is particularly useful for tomography applications. Since it can be implemented with synchrotron radiation and at laboratory micro-focus sources, it covers a wide range of applications. A limiting factor in its development has been the phase-retrieval step, which was often performed using methods with a limited regime of applicability, typically based on linearization. In this work, a much larger set of algorithms, which covers a wide range of cases (experimental parameters, objects and constraints), is compiled into a single toolbox – the HoloTomoToolbox – which is made publicly available. Importantly, the unified structure of the implemented phase-retrieval functions facilitates their use and performance test on different experimental data.

Electron beam range effects in SEM/EDX: Application to scratch-testing of coated carbides

1987 ◽  
Vol 45 ◽  
pp. 544-545
Author(s):  
J. Hefter
Keyword(s):  
Electron Beam ◽  
Protective Coatings ◽  
Cutting Tools ◽  
Scratch Test ◽  
Pixel Resolution ◽  
X Ray ◽  
X Ray Imaging ◽  
Test Investigation ◽  
And Performance ◽  
Tool Technology

The use of protective coatings to enhance the chemical and mechanical properties of materials is widely practiced in industry. For example, in cutting tool technology, the use of strongly adhering, specialized coatings has expanded the lifetime and performance of cutting tools. The most widely used technique for checking coating adherence is the scratch-test. Investigation of the scratch regions by SEM via both electron and x-ray imaging may assist in the determination of the failure mode region, i.e., within the coating, at an interface, or within the substrate.As an example of this approach, a cemented carbide was coated first with TiC (6 μm) and then with Al2O3 (2 μm). A scratch-test was carried out and the sample was analyzed using the JEOL JSM-840 analytical SEM at an electron beam energy of 20 keV. All secondary electron (grayscale) and x-ray images (256x256x8 pixel resolution) were taken using digital imaging hardware on a Tracor Northern TN-5500 system.

scholarly journals ROmodel: modeling robust optimization problems in Pyomo

2021 ◽  
Author(s):  
Johannes Wiebe ◽  
Ruth Misener
Keyword(s):  
Robust Optimization ◽  
Optimization Problems ◽  
Mathematical Formulation ◽  
Decision Rules ◽  
Matrix Representations ◽  
Uncertainty Sets ◽  
Linear Decision Rules ◽  
Algebraic Modeling Language ◽  
Integrate Data ◽  
Python Package

AbstractThis paper introduces ROmodel, an open source Python package extending the modeling capabilities of the algebraic modeling language Pyomo to robust optimization problems. ROmodel helps practitioners transition from deterministic to robust optimization through modeling objects which allow formulating robust models in close analogy to their mathematical formulation. ROmodel contains a library of commonly used uncertainty sets which can be generated using their matrix representations, but it also allows users to define custom uncertainty sets using Pyomo constraints. ROmodel supports adjustable variables via linear decision rules. The resulting models can be solved using ROmodels solvers which implement both the robust reformulation and cutting plane approach. ROmodel is a platform to implement and compare custom uncertainty sets and reformulations. We demonstrate ROmodel’s capabilities by applying it to six case studies. We implement custom uncertainty sets based on (warped) Gaussian processes to show how ROmodel can integrate data-driven models with optimization.

scholarly journals EUV Astrophysics with ALEXIS: The Wide View

1996 ◽  
Vol 152 ◽  
pp. 7-14
Author(s):  
Jeffrey Bloch
Keyword(s):  
Low Cost ◽  
Rapid Development ◽  
Normal Incidence ◽  
Space Mission ◽  
Los Alamos ◽  
X Ray ◽  
Ground Station ◽  
X Ray Imaging ◽  
And Performance ◽  
Imaging Sensors

The Array of Low Energy X-ray Imaging Sensors (ALEXIS) satellite is Los Alamos’ pathfinding small space mission achieving low cost and rapid development time for its technology demonstration and science goals. The ALEXIS satellite contains the ALEXIS telescope array, which consists of six EUV/ultrasoft X-ray telescopes utilizing normal incidence multilayer mirrors, microchannel plate detectors, and thin UV rejecting filters. Each telescope is tuned to a relatively narrow bandpass centered at either 130, 171, or 186 angstroms. Each telescope has a 33° field-of-view, and a resolution of ~ 0.25°. With each 50 s rotation of the satellite, the telescopes scan most of the anti-solar hemisphere of the sky. The spacecraft is controlled exclusively from a ground station located at Los Alamos.This paper discusses the characteristics and performance of the ALEXIS telescopes and the results from the mission in spite of the damage incurred to the spacecraft at launch.

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