#### Filter Results:

- Full text PDF available (47)

#### Publication Year

2004

2017

- This year (1)
- Last five years (18)

#### Publication Type

#### Co-author

#### Publication Venue

#### Key Phrases

Learn More

- J. William Helton, Jiawang Nie
- Math. Program.
- 2010

Let S = {x ∈ R n : g 1 (x) ≥ 0, · · · , gm(x) ≥ 0} be a semialgebraic set defined by multivariate polynomials g i (x). Assume S is convex, compact and has nonempty interior. Let S i = {x ∈ R n : g i (x) ≥ 0}, and ∂S (resp. ∂S i) be the boundary of S (resp. S i). This paper, as does the subject of semidefinite programming (SDP), concerns Linear Matrix… (More)

- Jiawang Nie, Markus Schweighofer
- J. Complexity
- 2007

Let S = {x ∈ R n | g 1 (x) ≥ 0,. .. , gm(x) ≥ 0} be a basic closed semialgebraic set defined by real polynomials g i. Putinar's Positivstellensatz says that, under a certain condition stronger than compactness of S, every real polynomial f positive on S posesses a representation f = P m i=0 σ i g i where g 0 := 1 and each σ i is a sum of squares of… (More)

- Jiawang Nie, Kristian Ranestad, Bernd Sturmfels
- Math. Program.
- 2010

Given a semidefinite program, specified by matrices with rational entries, each coordinate of its optimal solution is an algebraic number. We study the degree of the minimal polynomials of these algebraic numbers. Geometrically, this degree counts the critical points attained by a linear functional on a fixed rank locus in a linear space of symmetric… (More)

- Chen Ling, Jiawang Nie, Liqun Qi, Yinyu Ye
- SIAM Journal on Optimization
- 2009

This paper studies the so-called bi-quadratic optimization over unit spheres min x∈R n ,y∈R m subject to x = 1, y = 1. We show that this problem is NP-hard and there is no polynomial time algorithm returning a positive relative approximation bound. After that, we present various approximation methods based on semidefinite programming (SDP) relaxations. Our… (More)

- J. William Helton, Jiawang Nie
- SIAM Journal on Optimization
- 2009

A set S ⊆ R n is called to be Semidefinite (SDP) representable if S equals the projection of a set in higher dimensional space which is describable by some Linear Matrix Inequality (LMI). Clearly, if S is SDP representable, then S must be convex and semialgebraic (it is describable by conjunctions and disjunctions of polynomial equalities or inequalities).… (More)

- Jiawang Nie
- Math. Program.
- 2014

- Shaunak D. Bopardikar, Francesco Bullo, +40 authors Jochen Trumpf
- 2011

- Jiawang Nie, Li Wang
- SIAM J. Matrix Analysis Applications
- 2014

- Jiawang Nie
- Math. Program.
- 2013

Given polynomials f (x), g i (x), h j (x), we study how to minimize f (x) on the set S = {x ∈ R n : h 1 (x) = · · · = h m 1 (x) = 0, g 1 (x) ≥ 0,. .. , g m 2 (x) ≥ 0}. Let f min be the minimum of f on S. Suppose S is nonsingular and f min is achievable on S, which are true generically. This paper proposes a new type semidefinite programming (SDP) relaxation… (More)

- Chun-Feng Cui, Yu-Hong Dai, Jiawang Nie
- SIAM J. Matrix Analysis Applications
- 2014