On Robust Saddle-Point Criterion in Optimization Problems with Curvilinear Integral Functionals

In this paper, we introduce a new class of multi-dimensional robust optimization problems (named <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><mi>P...

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Bibliographic Details
Main Authors: Savin Treanţă, Koushik Das
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/9/15/1790
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Summary:In this paper, we introduce a new class of multi-dimensional robust optimization problems (named <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><mi>P</mi><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>) with mixed constraints implying second-order partial differential equations (PDEs) and inequations (PDIs). Moreover, we define an auxiliary (modified) class of robust control problems (named <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow><mo stretchy="false">(</mo><mi>P</mi><mo stretchy="false">)</mo></mrow><mrow><mo stretchy="false">(</mo><mover accent="true"><mi>b</mi><mo stretchy="false">¯</mo></mover><mo>,</mo><mover accent="true"><mi>c</mi><mo stretchy="false">¯</mo></mover><mo stretchy="false">)</mo></mrow></msub></semantics></math></inline-formula>), which is much easier to study, and provide some characterization results of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><mi>P</mi><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow><mo stretchy="false">(</mo><mi>P</mi><mo stretchy="false">)</mo></mrow><mrow><mo stretchy="false">(</mo><mover accent="true"><mi>b</mi><mo stretchy="false">¯</mo></mover><mo>,</mo><mover accent="true"><mi>c</mi><mo stretchy="false">¯</mo></mover><mo stretchy="false">)</mo></mrow></msub></semantics></math></inline-formula> by using the notions of normal weak robust optimal solution and robust saddle-point associated with a Lagrange functional corresponding to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow><mo stretchy="false">(</mo><mi>P</mi><mo stretchy="false">)</mo></mrow><mrow><mo stretchy="false">(</mo><mover accent="true"><mi>b</mi><mo stretchy="false">¯</mo></mover><mo>,</mo><mover accent="true"><mi>c</mi><mo stretchy="false">¯</mo></mover><mo stretchy="false">)</mo></mrow></msub></semantics></math></inline-formula>. For this aim, we consider path-independent curvilinear integral cost functionals and the notion of convexity associated with a curvilinear integral functional generated by a controlled closed (complete integrable) Lagrange 1-form.
ISSN:2227-7390