Convex Optimization

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위키데이터

• ID : Q58633368

말뭉치

1. A MOOC on convex optimization, CVX101, was run from 1/21/14 to 3/14/14.
2. This course concentrates on recognizing and solving convex optimization problems that arise in applications.
3. Because of their desirable properties, convex optimization problems can be solved with a variety of methods.
4. In layman's terms, the mathematical science of Convex Optimization is the study of how to make a good choice when confronted with conflicting requirements.
5. If a problem can be transformed to an equivalent convex optimization, then ability to visualize its geometry is acquired.
6. Study of equivalence, sameness, and uniqueness therefore pervade study of convex optimization.
7. It also includes many important problems as special case, such as OCO with long term constraints, stochastic constrained convex optimization, and deterministic constrained convex optimization.
8. The Wolfram Language provides the major convex optimization classes, their duals and sensitivity to constraint perturbation.
9. Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets.
10. Extensions of convex optimization include the optimization of biconvex, pseudo-convex, and quasiconvex functions.
11. Our implementation significantly lowers the barrier to using convex optimization problems in differentiable programs.
12. The lecture slides are adopted from Dr. Stephen Boyd's letcture notes on Convex Optimization at Standord University.
13. The basis pursuit minimization of (12.83) is a convex optimization problem that can be reformulated as a linear programming problem.
14. We first present our main theoretical results on how to train the program state of programmable quantum processors, either via convex optimization or first-order gradient-based algorithms.
15. When dealing with convex optimization with respect to positive operators, the standard approach is to map the problem to a form that is solvable via SDP (refs.
16. We employ our convex optimization procedures to find the optimal program state.
17. Here is a book devoted to well-structured and thus efficiently solvable convex optimization problems, with emphasis on conic quadratic and semidefinite programming.
18. In this lecture, we develop the basic mathematical preliminaries and tools to study convex optimization.
19. In this lecture, we introduce a class of cutting plane methods for convex optimization and present an analysis of a special case of it: the ellipsoid method.
20. CVXPY is a Python-embedded modeling language for convex optimization problems.
21. The course primarily focuses on techniques for formulating decision problems as convex optimization models that can be solved with existing software tools.
22. Convex optimization has many applications ranging from operations research and machine learning to quantum information theory.
23. Convex optimization problems, which involve the minimization of a convex function over a convex set, can be approximated in theory to any fixed precision in polynomial time.
24. The goal of this course is to investigate in-depth and to develop expert knowledge in the theory and algorithms for convex optimization.
25. CVXR is an R package that provides an object-oriented modeling language for convex optimization, similar to CVX , CVXPY , YALMIP , and Convex.jl .
26. It allows the user to formulate convex optimization problems in a natural mathematical syntax rather than the restrictive standard form required by most solvers.
27. The user is free to construct statistical estimators that are solutions to a convex optimization problem where there may not be a closed form solution or even an implementation.
28. We develop a sequential convex optimization algorithm to solve the resulting nonsmooth nonconvex optimization problem.
29. In this paper, we propose the first computationally efficient projection-free algorithm for bandit convex optimization (BCO) with a general convex constraint.
30. {In this paper, we propose the first computationally efficient projection-free algorithm for bandit convex optimization (BCO) with a general convex constraint.
31. Convex optimization studies the problem of minimizing a convex function over a convex set.
32. In the last few years, algorithms for convex optimization have revolutionized algorithm design, both for discrete and continuous optimization problems.
33. Surprisingly, algorithms for convex optimization have also been used to design counting problems over discrete objects such as matroids.
34. Simultaneously, algorithms for convex optimization have become central to many modern machine learning applications.
35. In this paper we lay the foundation of robust convex optimization.
36. U. This is consistent with a related phenomenon that has been studied in optimization—local strong convexity near the global optimum can improve the convergence rate of convex optimization (43).
37. x + &bgr; y ) = &agr; f i( x ) + &bgr; f i( y )), the problem is said to be one of convex optimization.
38. Note that linear programming is a special case of convex optimization, where the objective and constraint functions are all linear.
39. The next part, chapters 6 through 8, presents a treatment of applications of convex optimization in solving certain problems, arising in engineering, statistics, and mathematics.

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위키데이터

• ID : Q58633368