本文基于智慧教育背景下高职院校教学模式的现状,提出多元混合教学模式的创新构建策略。通过技术融合、创新创业教育与教学方法的创新,旨在推动教学模式根本变革。面对技术融合难度、教育理念转型及创新创业融合挑战,文章建议构建智慧...本文基于智慧教育背景下高职院校教学模式的现状,提出多元混合教学模式的创新构建策略。通过技术融合、创新创业教育与教学方法的创新,旨在推动教学模式根本变革。面对技术融合难度、教育理念转型及创新创业融合挑战,文章建议构建智慧教育生态系统、引领理念与管理机制变革、深化课程融合,并强化政策与资源保障,探讨了多元混合教学模式的创新路径与应对策略。Based on the current situation of the teaching mode of higher vocational colleges in the context of smart education, this paper proposes an innovative construction strategy of diversified mixed teaching models. Through the integration of technology, innovation and entrepreneurship education and the innovation of teaching methods, it aims to promote a fundamental change in teaching mode. In response to the challenges posed by the difficulties of technological integration, the transformation of educational philosophies, and the fusion of innovation and entrepreneurship, the paper suggests the construction of a smart education ecosystem. It advocates for leading changes in educational concepts and management mechanisms, deepening curriculum integration, and strengthening policy and resource support. Furthermore, it explores innovative pathways and strategies for addressing these challenges through diversified mixed teaching models.展开更多
本文提出了一种自适应惯性时变近端ADMM方法,旨在解决具有挑战性的非凸优化问题。该方法通过自适应调整惯性项和近端参数,增强了算法对非凸性和复杂结构的适应能力。我们的理论分析证明了在合适的条件下,算法能够实现全局收敛。数值实...本文提出了一种自适应惯性时变近端ADMM方法,旨在解决具有挑战性的非凸优化问题。该方法通过自适应调整惯性项和近端参数,增强了算法对非凸性和复杂结构的适应能力。我们的理论分析证明了在合适的条件下,算法能够实现全局收敛。数值实验部分展示了该方法在多个非凸优化问题上的有效性,包括稀疏信号恢复和图像处理任务。This paper proposes an adaptive inertial time-varying proximal ADMM method aimed at tackling challenging non-convex optimization problems. By adaptively adjusting the inertial term and proximal parameters, the algorithm enhances its adaptability to non-convexity and complex structures. Our theoretical analysis proves that the algorithm can achieve global convergence under suitable conditions. The numerical experiments demonstrate the effectiveness of this method on multiple non-convex optimization problems, including sparse signal recovery and image processing tasks.展开更多
文摘本文基于智慧教育背景下高职院校教学模式的现状,提出多元混合教学模式的创新构建策略。通过技术融合、创新创业教育与教学方法的创新,旨在推动教学模式根本变革。面对技术融合难度、教育理念转型及创新创业融合挑战,文章建议构建智慧教育生态系统、引领理念与管理机制变革、深化课程融合,并强化政策与资源保障,探讨了多元混合教学模式的创新路径与应对策略。Based on the current situation of the teaching mode of higher vocational colleges in the context of smart education, this paper proposes an innovative construction strategy of diversified mixed teaching models. Through the integration of technology, innovation and entrepreneurship education and the innovation of teaching methods, it aims to promote a fundamental change in teaching mode. In response to the challenges posed by the difficulties of technological integration, the transformation of educational philosophies, and the fusion of innovation and entrepreneurship, the paper suggests the construction of a smart education ecosystem. It advocates for leading changes in educational concepts and management mechanisms, deepening curriculum integration, and strengthening policy and resource support. Furthermore, it explores innovative pathways and strategies for addressing these challenges through diversified mixed teaching models.
文摘本文提出了一种自适应惯性时变近端ADMM方法,旨在解决具有挑战性的非凸优化问题。该方法通过自适应调整惯性项和近端参数,增强了算法对非凸性和复杂结构的适应能力。我们的理论分析证明了在合适的条件下,算法能够实现全局收敛。数值实验部分展示了该方法在多个非凸优化问题上的有效性,包括稀疏信号恢复和图像处理任务。This paper proposes an adaptive inertial time-varying proximal ADMM method aimed at tackling challenging non-convex optimization problems. By adaptively adjusting the inertial term and proximal parameters, the algorithm enhances its adaptability to non-convexity and complex structures. Our theoretical analysis proves that the algorithm can achieve global convergence under suitable conditions. The numerical experiments demonstrate the effectiveness of this method on multiple non-convex optimization problems, including sparse signal recovery and image processing tasks.
