Parameter-free robust optimization for the maximum-Sharpe portfolio problem

2021 ◽  
Author(s):  
Deepayan Chakrabarti
Keyword(s):  
Robust Optimization ◽  
Portfolio Problem

scholarly journals Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 187-209 ◽  
Author(s):  
Alireza Goli ◽  
◽  
Hasan Khademi Zare ◽  
Reza Tavakkoli-Moghaddam ◽  
Ahmad Sadeghieh ◽  
...  
Keyword(s):  
Robust Optimization ◽  
Optimization Algorithm ◽  
Product Portfolio ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Portfolio Problem

Unsupervised Learning and Robust Optimization in the Portfolio Problem

2020 ◽  
pp. 4-9
Author(s):  
Grigorii I. Beliavsky ◽  
Natalia V. Danilova ◽  
Alexey D. Logunov
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Robust Optimization ◽  
Unsupervised Learning ◽  
Optimization Problem ◽  
Clustering Algorithm ◽  
Learning Problems ◽  
Portfolio Problem ◽  
Robust Optimization Problem ◽  
Decision Making Model

Robust optimization in various problems of science and technology arises from the uncertainty of the parameters that determine the decision-making model. The parameters can be judged on the basis of a large number of observed examples. This challenge is to customize a solution based on a large number of examples that works well for examples that were not involved in customizing the solution. In this sense, the robust optimization problem belongs to machine learning problems. The paper uses a dichotomous clustering algorithm to determine the range of parameters for the optimal portfolio problem.

Robust optimization framework for cardinality constrained portfolio problem

2012 ◽  
Vol 12 (1) ◽  
pp. 91-99 ◽  
Author(s):  
Seyed Jafar Sadjadi ◽  
Mohsen Gharakhani ◽  
Ehram Safari
Keyword(s):  
Robust Optimization ◽  
Optimization Framework ◽  
Portfolio Problem

Network Congestion Minimization Models Based on Robust Optimization

2018 ◽  
Vol E101.B (3) ◽  
pp. 772-784 ◽  
Author(s):  
Bimal CHANDRA DAS ◽  
Satoshi TAKAHASHI ◽  
Eiji OKI ◽  
Masakazu MURAMATSU
Keyword(s):  
Robust Optimization ◽  
Network Congestion

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

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