Zobrazeno 1 - 10
of 297
pro vyhledávání: '"Vielma, Juan"'
Autor:
Lubin, Miles, Dowson, Oscar, Garcia, Joaquim Dias, Huchette, Joey, Legat, Benoît, Vielma, Juan Pablo
JuMP is an algebraic modeling language embedded in the Julia programming language. JuMP allows users to model optimization problems of a variety of kinds, including linear programming, integer programming, conic optimization, semidefinite programming
Externí odkaz:
http://arxiv.org/abs/2206.03866
The recent interior point algorithm by Dahl and Andersen [10] for nonsymmetric cones as well as earlier works [16,19] require derivative information from the conjugate of the barrier function of the cones in the problem. Besides a few special cases,
Externí odkaz:
http://arxiv.org/abs/2201.04121
Autor:
Aguayo, Ricardo1 ingcivilaguayo@gmail.com, Carlos Vielma, Juan1 juan.vielma@pucv.cl, Carvallo, Jorge1 jorge.carvallo@pucv.cl
Publikováno v:
Obras y Proyectos. 2024, Issue 35, p85-93. 9p.
Autor:
Papalexopoulos, Theodore, Tjandraatmadja, Christian, Anderson, Ross, Vielma, Juan Pablo, Belanger, David
Discrete black-box optimization problems are challenging for model-based optimization (MBO) algorithms, such as Bayesian optimization, due to the size of the search space and the need to satisfy combinatorial constraints. In particular, these methods
Externí odkaz:
http://arxiv.org/abs/2110.09569
In recent work, we provide computational arguments for expanding the class of proper cones recognized by conic optimization solvers, to permit simpler, smaller, more natural conic formulations. We define an exotic cone as a proper cone for which we c
Externí odkaz:
http://arxiv.org/abs/2107.04262
In polynomial optimization problems, nonnegativity constraints are typically handled using the sum of squares condition. This can be efficiently enforced using semidefinite programming formulations, or as more recently proposed by Papp and Yildiz [18
Externí odkaz:
http://arxiv.org/abs/2103.11499
Spectral functions on Euclidean Jordan algebras arise frequently in convex models. Despite the success of primal-dual conic interior point solvers, there has been little work on enabling direct support for spectral cones, i.e. proper nonsymmetric con
Externí odkaz:
http://arxiv.org/abs/2103.04104
Mixed-integer convex representable (MICP-R) sets are those sets that can be represented exactly through a mixed-integer convex programming formulation. Following up on recent work by Lubin et al. (2017, 2020) we investigate structural geometric prope
Externí odkaz:
http://arxiv.org/abs/2103.03379
Autor:
Tjandraatmadja, Christian, Anderson, Ross, Huchette, Joey, Ma, Will, Patel, Krunal, Vielma, Juan Pablo
We improve the effectiveness of propagation- and linear-optimization-based neural network verification algorithms with a new tightened convex relaxation for ReLU neurons. Unlike previous single-neuron relaxations which focus only on the univariate in
Externí odkaz:
http://arxiv.org/abs/2006.14076
Many convex optimization problems can be represented through conic extended formulations with auxiliary variables and constraints using only the small number of standard cones recognized by advanced conic solvers such as MOSEK 9. Such extended formul
Externí odkaz:
http://arxiv.org/abs/2005.01136