Curtin Research Publications: Recent submissions
Now showing items 2331-2340 of 70725
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(2020)Lagrangian variational inequalities feature both primal and dual elements in expressing first-order conditions for optimality in a wide variety of settings where “multipliers” in a very general sense need to be brought ...
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(2020)This study relaxes the distributional assumption of the return of the risky asset, to arrive at the optimal portfolio. Studies of portfolio selection models have typically assumed that stock returns conform to the normal ...
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(2020)Stochastic optimization models based on risk-averse measures are of essential importance in financial management and business operations. This paper studies new algorithms for a popular class of these models, namely, the ...
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(2021)A modified Krasnoselski-Mann iterative algorithm is proposed for solving the split common fixed-point problem for quasi-nonexpansive operators. A parameter sequence is introduced to enhance convergence. It is shown that ...
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(2021)A computational method is developed for solving time consistent distributionally robust multistage stochastic linear programs with discrete distribution. The stochastic structure of the uncertain parameters is described ...
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(2021)Joint chance-constrained optimization problems under discrete distributions arise frequently in financial management and business operations. These problems can be reformulated as mixed-integer programs. The size of ...
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(2021)We consider the problem of minimizing the sum of an average of a large number of smooth convex component functions and a possibly nonsmooth convex function that admits a simple proximal mapping. This class of problems ...
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(2021)The paper studies the progressive decoupling algorithm (PDA) of Rockafellar and focuses on the elicited version of the algorithm. Based on a generalized Yosida-regularization of Spingarn’s partial inverse of an elicitable ...
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(2022)Many machine learning problems can be formulated as minimizing the sum of a function and a non-smooth regularization term. Proximal stochastic gradient methods are popular for solving such composite optimization problems. ...
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(2022)We explore a new model in mathematical programming in which a separabie convex piecewise quadratic function is minimized subject to linear constraints. The discussion includes basic theories such as duality, optimality, ...