In this paper, we consider the linear programming (LP) formulation for deep reinforcement learning. The number of constraints depends on size state and action spaces, which makes problem intractable in large or continuous environments. general augmented Lagrangian method suffers double-sampling obstacle solving program. Motivated from updates multipliers, overcome obstacles minimizing function ...

This paper presents a discussion on the properties of collocation meshfree method, Updated Lagrangian Taylor-SPH (UL-TSPH), for dynamic problems in solid mechanics. The PDEs are written mixed form terms stress and velocity elastodynamics problems. Two sets particles used to discretize partial differential equations, resulting avoiding tensile instability inherent classical SPH formulations. Num...

This paper introduces the Lagrangian relaxation method to solve multiobjective optimization problems. It is often required use appropriate technique determine multipliers in that leads finding optimal solution problem. Our analysis aims find a suitable generate multipliers, and later these are used Multiobjective We propose search-based Lagrange multipliers. In our paper, we choose well-known s...

The breathing-mode isoscalar giant monopole resonance (GMR) is investigated using the generator coordinate method within the relativistic mean-field (RMF) theory. Employing the Lagrangian models of the nonlinear-σ model (NLσ), the scalarvector interaction model (SVI) and the σ-ω coupling model (SIGO), we show that each Lagrangian model exhibits a distinctly different GMR response. Consequently,...