We pay a special attention to the risk measure called the superquantile (also known as the "Conditional Value at Risk") and we show how, in various contexts, it may enforce robustness for decision-making under uncertainty. In this thesis, we consider nonsmooth optimization problems involving risk measures and coming from statistical learning applications. This field is currently challenged by applications in artificial intelligence and data science, where risk management has become a crucial issue. This thesis deals with optimization under uncertainty, which has a long history in operations research and mathematical optimization. The proposed approach is applied to a numerical illustrating example and a chemical process case study to demonstrate its capability of handling the SOCP and the SMPC implementation. Moreover, while applying the proposed approach to the SMPC, the SOCP involving the RNN-based model is solved repeatedly at different sampling instants, based on different initial system states. Subsequently, the SOCP involving the RNN-based model can be solved using a deterministic nonlinear optimization solver. Afterwards, the RNN-based model is embedded into the probabilistic constraint of the SOCP. Then, the sample average approximation (SAA) method is used to build the RNN-based surrogate model for the quantile function. In the proposed approach, the joint chance constraint (JCC) in a SOCP is first reformulated as a quantile-based inequality. This tutorial book is very useful for NIELIT computer courses, Computer Engineering students and professional interested to learn Python, a high level modern language.ġ.2 The Python Interpreter and Idle, Part Iġ.3 Whirlwind Introduction To Types and Functionsġ.A recurrent neural network (RNN)-based approach is proposed in this paper to handle joint chance-constrained stochastic optimal control problems (SOCP) and stochastic model predictive control (SMPC) implementations. In new syllabus 2021 -2022 of NIELIT (DOEACC) O level course, Python language have been added in place of C++.
In this set of tutorials we will learn to write your own computer programs, so we can give the computer instructions to react in the way we want. Still the underlying operations are determining by the design of the program we are given. In each application, the computer reacts in many different ways to your input, from the keyboard, mouse or a file. You are probably using computers to do all sorts of interesting things. Harrington, notes e-book for PDF free download. Hands on Python Tutorial for Beginners by Dr.
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