현재 이 사이트는 활발히 개발 중입니다. 문제를 발견하시면 신고해 주세요! - 문제 신고하기

NONLINEAR OPTIMIZATION I

COMPSCI/ISYE/MATH/STAT 726
과목 설명

Theory and algorithms for nonlinear optimization, focusing on unconstrained optimization. Line-search and trust-region methods; quasi-Newton methods; conjugate-gradient and limited-memory methods for large-scale problems; derivative-free optimization; algorithms for least-squares problems and nonlinear equations; gradient projection algorithms for bound-constrained problems; and simple penalty methods for nonlinearly constrained optimization. Students are strongly encouraged to have knowledge of linear algebra and familiarity with basic mathematical analysis.

선수과목

Graduate/professional standing

충족 요건
학점

미보고

개설 시기

미보고

평점
3.31

1.12% 과거 데이터 대비

수료율
100%

2.5% 과거 데이터 대비

A 비율
14.29%

-56.36% 과거 데이터 대비

학급 규모
21

-40.69% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

NONLINEAR OPTIMIZATION II

COMPSCI/ISYE/MATH 730

Theory and algorithms for nonlinearly constrained optimization. Relevant geometric concepts, including tangent and normal cones, theorems of the alternative, and separation results. Constraint qualifications. Geometric and algebraic expression of first-order optimality conditions. Second-order optimality conditions. Duality. Nonlinear programming algorithms: merit functions and filters; interior-point, augmented Lagrangian, and sequential quadratic programming algorithms.

INTRODUCTION TO OPTIMIZATION

COMPSCI/ECE/ISYE 524

Introduction to mathematical optimization from a modeling and solution perspective. Formulation of applications as discrete and continuous optimization problems and equilibrium models. Survey and appropriate usage of basic algorithms, data and software tools, including modeling languages and subroutine libraries.

LINEAR ALGEBRA AND OPTIMIZATION

MATH 345

Introduction to linear algebra, differential calculus in several variables, and basic optimization theory with applications to data science and related topics. Vectors, analytic geometry, matrices, linear functions, linear independence, orthogonality, inverses, partial derivatives and gradients, Taylor approximation, gradient descent, Lagrange multipliers, clustering, regression, classification. Implementation in Python.

THEORETICAL FOUNDATIONS OF LARGE-SCALE MACHINE LEARNING

ECE 826

Mathematical foundations of large-scale machine learning and optimization. Focus on recent texts in machine learning, optimization, and randomized algorithms, focused on tradeoffs that are driving algorithmic design in this new discipline. These trade-offs revolve around speed of convergence, statistical accuracy, robustness, scalability, algorithmic complexity, and implementation.

LINEAR OPTIMIZATION

COMPSCI/ISYE/MATH/STAT 525

Introduces optimization problems whose constraints are expressed by linear inequalities. Develops geometric and algebraic insights into the structure of the problem, with an emphasis on formal proofs. Presents the theory behind the simplex method, the main algorithm used to solve linear optimization problems. Explores duality theory and theorems of the alternatives.