One-stage stochastic linear complementarity problem (SLCP) is a special case of multi-stage problem, which has important applications in economic engineering and operations management. In this paper, we establish asymptotic analysis results sample-average approximation (SAA) estimator for the SLCP. The normality stochastic-constrained optimization are extended to SLCP model then conditions, ensure convergence distribution multivariate normal with zero mean vector covariance matrix, obtained....

The variational inequality framework holds significant prominence across various domains including economic finance, network transportation, and game theory. In addition, a novel approach utilizing neural model is introduced in the current work to address box constrained problem. Initially, original problem reformulated into nonsmooth equation, following which meticulously devised tackle this equation. This study comprehensively investigated inherent characteristics properties of model....

<abstract><p>In this paper, an efficient artificial neural network is proposed for solving a generalized vertical complementarity problem. Based on the properties of log-exponential function, problem reformulated in terms unconstrained minimization The existence and convergence trajectory are addressed detail. In addition, it also proved that if has equilibrium point under some initial condition, asymptotically stable or exponentially certain conditions. At end simulation results...

Counterfactual regret minimization (CFR) is an effective algorithm for solving extensive‐form games with imperfect information (IIEGs). However, CFR only allowed to be applied in known environments, where the transition function of chance player and reward terminal node IIEGs are known. In uncertain situations, such as reinforcement learning (RL) problems, not applicable. Thus, applying unknown environments a significant challenge that can also address some difficulties real world....

We study a class of binary treatment choice problems with partial identification, through the lens robust (multiple prior) Bayesian analysis. use convenient set prior distributions to derive ex-ante and ex-post Bayes decision rules, both for makers who can randomize cannot. Our main messages are as follows: First, rules do not tend agree in general, whether or randomized allowed. Second, assignment some data realizations be optimal and, perhaps more surprisingly, problems. Therefore, it is...

In this paper, the cold launch test platform is taken as research object, theoptimization model of established by using topology optimization method. First,the goal structure determined, and then constraints are carriedout. Finally, optimized non-optimized areas identified, result processed. Afteroptimization, quality was reduced 2.9 tons under conditions use.

Ontology ranking is one of the important functions ontology search engines, which ranks searched ontologies based on model applied. A good method can help users acquire exactly required from a considerable amount results, efficiently. Existing approaches to rank take only single aspect into consideration and ignore users' personalised demands, hence produce unsatisfactory result. It believed that, factors that influence importance demands both need be considered comprehensively in ranking....

Ontology ranking is one of the important functions ontology search engines, which ranks searched ontologies based on model applied. A good method can help users acquire exactly required from a considerable amount results, efficiently. Existing approaches to rank take only single aspect into consideration and ignore users' personalised demands, hence produce unsatisfactory result. It believed that, factors that influence importance demands both need be considered comprehensively in ranking....

Counterfactual regret minimization (CFR) is an effective algorithm for solving extensive games with imperfect information (IIEGs). However, CFR only allowed to be applied in known environments, where the transition function of chance player and reward terminal node IIEGs are known. In uncertain situations, such as reinforcement learning (RL) problems, not applicable. Thus, applying unknown environments a significant challenge that can also address some difficulties real world. Currently,...