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2010

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Approximation algorithms for homogeneous polynomial optimization with quadratic constraints

13 years 11 months ago

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In this paper, we consider approximation algorithms for optimizing a generic multi-variate homogeneous polynomial function, subject to homogeneous quadratic constraints. Such opti...

Simai He, Zhening Li, Shuzhong Zhang

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Id	Views	Title	Status

1	172	Approximation algorithms for homogeneous polynomial optimization with quadratic constraints	claim
2	163	Speeding up IP-based algorithms for constrained quadratic 0-1 optimization	claim
3	162	Approximation accuracy, gradient methods, and error bound for structured convex optimization	claim
4	157	Newton's method for generalized equations: a sequential implicit function theorem	claim
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7	150	The algebraic degree of semidefinite programming	claim
8	149	Copositive programming motivated bounds on the stability and the chromatic numbers	claim
9	146	A retrospective trust-region method for unconstrained optimization	claim
10	145	The traveling salesman problem with pickup and delivery: polyhedral results and a branch-and-cut algorithm	claim
11	135	An inexact Newton method for nonconvex equality constrained optimization	claim
12	132	Exploiting group symmetry in semidefinite programming relaxations of the quadratic assignment problem	claim
13	128	Perspective reformulations of mixed integer nonlinear programs with indicator variables	claim
14	128	Copositivity cuts for improving SDP bounds on the clique number	claim
15	126	Two row mixed-integer cuts via lifting	claim
16	119	Efficient and fair routing for mesh networks	claim
17	116	Separation algorithms for 0-1 knapsack polytopes	claim
18	116	New techniques for cost sharing in combinatorial optimization games	claim
19	110	Computable representations for convex hulls of low-dimensional quadratic forms	claim
20	103	Relations between facets of low- and high-dimensional group problems	claim
21	100	Strong valid inequalities for orthogonal disjunctions and bilinear covering sets	claim
22	99	Separation, dimension, and facet algorithms for node flow polyhedra	claim
23	98	Risk-averse dynamic programming for Markov decision processes	claim
24	97	Semidefinite representation of convex sets	claim
25	94	MIR closures of polyhedral sets	claim
26	92	Single item lot-sizing with non-decreasing capacities	claim
27	85	The effect of deterministic noise in subgradient methods	claim
28	85	Solving Max-Cut to optimality by intersecting semidefinite and polyhedral relaxations	claim
29	82	Conic mixed-integer rounding cuts	claim
30	81	Lifting inequalities: a framework for generating strong cuts for nonlinear programs	claim
31	80	Nonlinear programming without a penalty function or a filter	claim
32	80	Mixing MIR inequalities with two divisible coefficients	claim
33	79	MIP reformulations of the probabilistic set covering problem	claim
34	74	Lattice based extended formulations for integer linear equality systems	claim
35	70	On totally Fenchel unstable functions in finite dimensional spaces	claim
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37	66	Generating and measuring instances of hard semidefinite programs	claim
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39	57	Stabilized sequential quadratic programming for optimization and a stabilized Newton-type method for variational problems	claim
40	52	Global minimization using an Augmented Lagrangian method with variable lower-level constraints	claim

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