A5003656133- Advanced Optimization Algorithms Research
- Quantum Computing Algorithms and Architecture
- Sparse and Compressive Sensing Techniques
- Quantum Information and Cryptography
- Optimal Power Flow Distribution
- Complexity and Algorithms in Graphs
- Game Theory and Applications
- Advanced Control Systems Optimization
- Smart Grid Energy Management
- Power System Optimization and Stability
- Auction Theory and Applications
- Quantum Mechanics and Applications
- Stochastic Gradient Optimization Techniques
- Constraint Satisfaction and Optimization
- Transportation and Mobility Innovations
- Optimization and Variational Analysis
- Vehicle Routing Optimization Methods
- Complex Systems and Time Series Analysis
- Data Management and Algorithms
- Scheduling and Optimization Algorithms
- Control Systems and Identification
- Fault Detection and Control Systems
- Anomaly Detection Techniques and Applications
- Formal Methods in Verification
- Optimization and Search Problems
Czech Technical University in Prague
2018-2025
Imperial College London
2025
Delft University of Technology
2020-2023
Institute of Electrical and Electronics Engineers
2021-2023
Antea Group (France)
2023
University of California, Los Angeles
2023
Canadian Standards Association
2020-2021
IBM Research - Ireland
2014-2020
IBM (United States)
2014
University of Nottingham
2007-2011
This article outlines our point of view regarding the applicability, state-of-the-art, and potential quantum computing for problems in finance. We provide an introduction to as well a survey on problem classes finance that are computationally challenging classically which algorithms promising. In main part, we describe detail specific applications arising financial services, such those involving simulation, optimization, machine learning problems. addition, include demonstrations IBM Quantum...
Journal Article Handbook of Approximation Algorithms and Metaheuristics Get access Teofilo F. GonzalezHandbook Metaheuristics. Chapman & Hall/CRC Computer ( 2007). ISBN: 9781584885504. £82 1432 pp. Hardcover. Jakub Mareček University Nottingham, UK Search for other works by this author on: Oxford Academic Google Scholar The Journal, Volume 53, Issue 8, October 2010, Pages 1338–1339, https://doi.org/10.1093/comjnl/bxp121 Published: 28 January 2010
There is an increasing interest in quantum algorithms for problems of integer programming and combinatorial optimization. Classical solvers such employ relaxations, which replace binary variables with continuous ones, instance the form higher-dimensional matrix-valued (semidefinite programming). Under Unique Games Conjecture, these relaxations often provide best performance ratios available classically polynomial time. Here, we discuss how to warm-start optimization initial state...
Formulating the alternating current optimal power flow (ACOPF) as a polynomial optimization problem makes it possible to solve large instances in practice and guarantee asymptotic convergence theory. We formulate ACOPF degree-two program study two approaches solving via convexifications. In first approach, we tighten first-order relaxation of nonconvex quadratic by adding valid inequalities. second exploit structure using sparse variant Lasserre's hierarchy. This allows us up 39 buses global...
<ns4:p>Introduction A major obstacle in understanding the relationship between diet and non-communicable diseases (NCDs) lies subjectivity bias traditional dietary intake assessment methods. Food diaries, 24-hour food recall interviews, frequency questionnaires (FFQ) result under- over-reporting of nutrient energy intake, compromising accuracy studies linking to NCDs. The emergence image classification technology has facilitated a new approach which addresses subjective limitations...
In power system steady-state estimation (PSSE), one needs to consider (1) the need for robust statistics, (2) nonconvex transmission constraints, (3) fast-varying nature of inputs, and corresponding track optimal trajectories as closely possible. combination, these challenges have not been considered, yet. this paper, we address all three challenges. The robustness is addressed by using an approach based on so-called Huber model. non-convexity problem, which results in first order methods...
Open quantum systems are a rich area of research on the intersection mechanics and stochastic analysis. We unify multiple views controlled open within framework bilinear dynamical systems. define corresponding notions identifiability from results state tomography, obtained in many copies initial state, under subsequences varying lengths control signals. explain extend work using either spectral criteria, criteria based Hankel matrix, frequency-domain to parameter estimation master equations...
Bias evaluation is fundamental to trustworthy AI, both in terms of checking data quality and the outputs AI systems. In testing quality, for example, one may study a distance given dataset, viewed as distribution, ground-truth reference dataset. However, classical metrics, such Total Variation Wasserstein distances, are known have high sample complexities and, therefore, fail provide meaningful distinction many practical scenarios. this paper, we propose new notion distance, Maximum Subgroup...
Sample complexity of bias estimation is a lower bound on the runtime any detection method. Many regulatory frameworks require to be tested for all subgroups, whose number grows exponentially with protected attributes. Unless one wishes run doubly-exponential run-time, should like have polynomial single subgroup. At same time, reference data may based surveys, and thus come non-trivial uncertainty. Here, we reformulate as point-to-subspace problem space measures show that, supremum norm, it...
Monte Carlo sampling is a powerful toolbox of algorithmic techniques widely used for number applications wherein some noisy quantity, or summary statistic thereof, sought to be estimated. In this paper, we survey the literature implementing procedures using quantum circuits, focusing on potential obtain advantage in computational speed these procedures. We revisit algorithms that could replace classical and then consider both existing realizations include adaptive enhancements as...
The ranking of nodes in a network according to their centrality or ``importance'' is classic problem that has attracted the interest different scientific communities last decades. COVID-19 pandemic recently rejuvenated this problem, as may be used decide who should tested, vaccinated, first, population asymptomatic individuals. In paper, we review methods for node and compare performance benchmark considers community-based structure society. outcome procedure then which individuals possibly...
There has been much recent interest in near-term applications of quantum computers, i.e., using circuits that have short decoherence times due to hardware limitations. Variational algorithms (VQA), wherein an optimization algorithm implemented on a classical computer evaluates parametrized circuit as objective function, are leading framework this space. An enormous breadth proposed for solving range problems machine learning, forecasting, applied physics, and combinatorial optimization,...
Kalman filter is a key tool for time-series forecasting and analysis. We show that the dependence of prediction on past decaying exponentially, whenever process noise non-degenerate. Therefore, may be approximated by regression few recent observations. Surprisingly, we also having some essential exponential decay. With no noise, it happen forecast depends all uniformly, which makes more difficult. Based this insight, devise an on-line algorithm improper learning linear dynamical system...