Course Introduction:<p>    <span>本</span><span>课</span><span>程是</span>IEEE<span>班和人工智能班</span><span>本科生基</span><span>础</span><span>理</span><span>论课</span><span>。本</span><span>课</span><span>程将介</span><span>绍</span><span>线性规划、</span><span>凸</span><span>优</span><span>化的基</span><span>础</span><span>理</span><span>论</span><span>、算法。</span><span>近年来，随着科学与工程的</span><span>进</span><span>步，凸</span><span>优</span><span>化理</span><span>论</span><span>与方法的研究迅猛</span><span>发</span><span>展，在科学与工程</span><span>计</span><span>算，</span> <span>数据科学，信号和</span><span>图</span><span>像</span><span>处</span><span>理，管理科学等</span><span>诸</span><span>多</span><span>领</span><span>域中得到了广泛</span><span>应</span><span>用。通</span><span>过</span><span>本</span><span>课</span><span>程的学</span><span>习</span><span>，</span><span>一方面，</span><span>掌握凸</span><span>优</span><span>化的基本概念，</span><span>凸</span><span>集和凸函数的定义与判别，线性代数中矩阵分解特别是特征值分解问题，线性规划，</span><span>对</span><span>偶理</span><span>论</span><span>，</span><span>凸</span><span>优</span><span>化的最</span><span>优</span><span>条件等；另一方面，掌握</span><span>典型的优化算法，包括梯度法和线搜索法，牛顿算法，还包括</span><span>典型的几</span><span>类</span><span>凸</span><span>优</span><span>化</span><span>问题</span><span>的判</span><span>别</span><span>及其</span><span>计</span><span>算方法</span><span>，并</span><span>熟悉相关</span><span>计算软件</span><span>。</span><span>课</span><span>程教学的目</span><span>标</span><span>是</span><span>让</span><span>学生懂得凸</span><span>优</span><span>化的基本概念，从而建立把</span><span>实际问题转</span><span>化</span><span>为</span><span>或近似</span><span>为</span><span>凸</span><span>优</span><span>化</span><span>问题的能力，为日后的科学研究与工业应用打下坚实的基础。</span></p>
Testing Method:笔试
School Year:2021-2022
Semester:Autumn Term
Course number:(2021-2022-1)-CS2601-1
Credits:3.0
Course Type:Undergraduate Course
Top-Quality Courses or Not:no
Maximum Number of Students:100
Required Class Hours:48.0