<![CDATA[Convex Optimization plays a crucial role in various scientific and industrial applications, such as economics, engineering, and computer science, with a primary focus on linear and quadratic optimization. This study examines the characteristics and comparison between linear and quadratic optimization, two main subclasses of convex optimization. Linear optimization (LP) is characterized by a linear objective function and linear constraints, where classical methods such as Simplex and Interior-Point are used for efficient solutions. In contrast, quadratic optimization (QP) involves a convex quadratic objective function with linear constraints, requiring more complex methods such as Karush-Kuhn-Tucker (KKT) factorization, Schur-Complement, Null-Space, Active-Set, and Interior-Point for solving. This paper summarizes various solution methods for both types of optimizations and compares their strengths and limitations. The key findings indicate that linear optimization is simpler and more efficient, while quadratic optimization offers greater flexibility in modeling problems with more complex structures. The study concludes that a deep understanding of both approaches is essential for the development of more efficient and applicable convex optimization algorithms.]]>
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