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MM algorithm
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<p>The MM algorithm is an iterative <a href="/facts/Optimization/oRn8Iv5I">optimization</a> method which exploits the <a href="/facts/Convex_function/IbzG5SLF">convexity</a> of a function in order to find its maxima or minima. The MM stands for “Majorize-Minimization” or “Minorize-Maximization”, depending on whether the desired optimization is a minimization or a maximization. Despite the name, MM itself is not an algorithm, but a description of how to construct an <a href="/facts/Optimization_algorithm/oRn8Iv5I">optimization algorithm</a>. </p><p>The <a href="/facts/Expectation%25E2%2580%2593maximization_algorithm/32P2bdBc">expectation–maximization algorithm</a> can be treated as a special case of the MM algorithm. However, in the EM algorithm <a href="/facts/Conditional_expectation/3GxG1Q3x">conditional expectations</a> are usually involved, while in the MM algorithm convexity and inequalities are the main focus, and it is easier to understand and apply in most cases. </p>