Lecture Convex optimization - Chapter: Statistical estimation. This chapter presents the following content: Maximum likelihood estimation, optimal detector design, experiment design. Please refer to the documentation for more details.

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Lecture Convex optimization - Chapter: Convex sets. This chapter presents the following content: Affine and convex sets, some important examples, operations that preserve convexity, generalized inequalities, separating and supporting hyperplanes, dual cones and generalized inequalities.

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Lecture Convex optimization - Chapter: Interior-point methods. In this chapter we discuss interior-point methods for solving convex optimization problems that include inequality constraints. This chapter presents the following content: Inequality constrained minimization, logarithmic barrier function and central path, barrier method, feasibility and phase I methods, complexity analysis via self-concordance, generalized inequalities.

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Lecture Convex optimization - Chapter: Conclusions. This chapter presents the following content: Main ideas of the course, importance of modeling in optimization. Please refer to the documentation for more details.

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Lecture Convex optimization - Chapter: Equality constrained minimization. This chapter presents the following content: Equality constrained minimization, eliminating equality constraints, Newton’s method with equality constraints, infeasible start Newton method, implementation.

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Lecture Convex optimization - Chapter: Unconstrained minimization. This chapter presents the following content: Terminology and assumptions, gradient descent method, steepest descent method, Newton’s method, self-concordant functions, implementation.

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Lecture Convex optimization - Chapter: Filter design. This chapter presents the following content: FIR filters, Chebychev design, linear phase filter design, equalizer design, filter magnitude specifications. Please refer to the documentation for more details.

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Lecture Convex optimization - Chapter: Convex functions. This chapter presents the following content: Basic properties and examples, operations that preserve convexity, the conjugate function, quasiconvex functions, log-concave and log-convex functions, convexity with respect to generalized inequalities.

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Lecture Convex optimization - Chapter: Geometric problems. This chapter presents the following content: Extremal volume ellipsoids, centering, classification, placement and facility location. Please refer to the documentation for more details.

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Lecture Convex optimization - Chapter: Introduction. This chapter presents the following content: Mathematical optimization, least-squares and linear programming, convex optimization, example, course goals and topics, nonlinear optimization, brief history of convex optimization.

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Lecture Convex optimization - Chapter: Stochastic programming. This chapter presents the following content: Stochastic programming, ’certainty equivalent’ problem, violation/shortfall constraints and penalties, Monte Carlo sampling methods, validation.

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Bài viết Xu thế biến đổi phi tuyến tính của mưa cực đoan trên khu vực Việt Nam trình bày nghiên cứu xu thế biến đổi của mưa lớn đặt ra là một vấn đề có ý nghĩa thực tiễn và khoa học cao. Tuy nhiên, các nghiên cứu về xu thế mưa lớn chủ yếu dựa trên phương pháp phân tích xu thế tuyến tính hoặc phương pháp phi tham số Sen.

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