国际学生入学条件
A Master's degree in combinatorics and optimization, or in a closely related field, with a minimum 89% average in Master's level coursework.
Completion of a master's thesis.
It is essential that the application for admission into the PhD program contains evidence of research ability or potential.
Three references, normally from academic sources
Proof of English language proficiency, if applicable. TOEFL 90 (writing 25, speaking 25), IELTS 7.0 (writing 6.5, speaking 6.5)
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雅思考试总分
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雅思考试指南
- 雅思总分:7
- 托福网考总分:90
- 托福笔试总分:160
- 其他语言考试:PTE (Academic) - 63 (writing 65, speaking 65)
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申请截止日期: 请与IDP顾问联系以获取详细信息。
课程简介
持续优化是解决从生物分子设计到投资组合管理的现实世界问题的核心数学科学。连续优化意味着找到一个或多个实变量的函数的最小值或最大值
Combinatorics is the study of discrete structures and their properties. Many modern scientific advances have employed combinatorial structures to model the physical world, and recent advances in computational technology have made such investigations feasible. In particular, since computers process discrete data, combinatorics has become indispensable to computer science. Optimization, or mathematical programming, is the study of maximizing and minimizing functions subject to specified boundary conditions or constraints. With the emergence of computers, optimization experienced a dramatic growth as a mathematical theory, enhancing both combinatorics and classical analysis. The functions to be optimized arise in engineering, the physical and management sciences, and in various branches of mathematics. The PhD involves about two years of grad courses followed by research and a dissertation, and typically lasts four years.<br><br>Continuous optimization is the core mathematical science for real-world problems ranging from design of biomolecules to management of investment portfolios. Continuous optimization means finding the minimum or maximum value of a function of one or many real variables, subject to constraints. The constraints usually take the form of equations or inequalities. Continuous optimization has been the subject of study by mathematicians since Newton, Lagrange and Bernoulli. One major focus of the continuous optimization group at Waterloo is convex optimization, that is, continuous optimization in the case that the objective function and feasible set are both convex. Convex optimization problems have widespread applications in practice and also have special properties that make them amenable to sophisticated analysis and powerful algorithms. Members of the group have carried out fundamental work in convex optimization including new and more efficient algorithms for convex optimization and understanding of the most fundamental properties of convex sets such as properties of the set of positive semidefinite matrices.
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