Search Results - 90C25 Mathematical Programming - convex programming*

On semidefinite programming relaxations for a class of robust SOS-convex polynomial optimization problems by Sun, Xiangkai, Huang, Jiayi, Teo, Kok Lay

“…In this paper, we deal with a new class of SOS-convex (sum of squares convex) polynomial optimization problems with spectrahedral uncertainty data in both the objective and constraints…”
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A slightly lifted convex relaxation for nonconvex quadratic programming with ball constraints by Burer, Samuel

“… However, there is no known explicit, tractable, exact convex representation for m≥3. In this paper, we construct a new, polynomially sized semidefinite relaxation for all m, which does not employ a disjunctive approach…”
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Data-driven inverse optimization with imperfect information by Mohajerin Esfahani, Peyman, Shafieezadeh-Abadeh, Soroosh, Hanasusanto, Grani A., Kuhn, Daniel

“…In data-driven inverse optimization an observer aims to learn the preferences of an agent who solves a parametric optimization problem depending on an…”
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Shapes and recession cones in mixed-integer convex representability by Zadik, Ilias, Lubin, Miles, Vielma, Juan Pablo

“…Mixed-integer convex representable (MICP-R) sets are those sets that can be represented exactly through a mixed-integer convex programming formulation…”
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Branch-and-bound performance estimation programming: a unified methodology for constructing optimal optimization methods by Das Gupta, Shuvomoy, Van Parys, Bart P. G., Ryu, Ernest K.

“…We present the Branch-and-Bound Performance Estimation Programming (BnB-PEP), a unified methodology for constructing optimal first-order methods for convex and nonconvex optimization…”
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Convex hull results on quadratic programs with non-intersecting constraints by Joyce, Alexander, Yang, Boshi

“…Let F ⊆ R n be a nonempty closed set. Understanding the structure of the closed convex hull C ¯ ( F ) : = conv ¯ { ( x , x x T ) | x ∈ F…”
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Complexity of first-order inexact Lagrangian and penalty methods for conic convex programming by Necoara, I., Patrascu, A., Glineur, F.

“…In this paper we present a complete iteration complexity analysis of inexact first-order Lagrangian and penalty methods for solving cone-constrained convex problems that have or…”
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New analysis and results for the Frank–Wolfe method by Freund, Robert M., Grigas, Paul

“…We present new results for the Frank–Wolfe method (also known as the conditional gradient method). We derive computational guarantees for arbitrary step-size…”
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A Benson-type algorithm for bounded convex vector optimization problems with vertex selection by Dörfler, Daniel, Löhne, Andreas, Schneider, Christopher, Weißing, Benjamin

“…We present an algorithm for approximately solving bounded convex vector optimization problems…”
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Mathematical programming with Semilocally Subconvex functions over cones by Sharma, Vani, Chaudhary, Mamta, Grover, Meetu Bhatia

“… Then we investigate the optimalsolutions of the mathematical programming problem (MP) over cones using these functions, directional derivatives, andthe alternative theorem…”
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Two Convergent Primal–Dual Hybrid Gradient Type Methods for Convex Programming with Linear Constraints by Sun, Min, Liu, Jing, Tian, Maoying

“…As an effective tool for convex programming, the primal–dual hybrid gradient (PDHG) method has been widely applied in science and engineering computing field…”
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NP-hardness of deciding convexity of quartic polynomials and related problems by Ahmadi, Amir Ali, Olshevsky, Alex, Parrilo, Pablo A., Tsitsiklis, John N.

“…) is globally convex. This solves a problem that has been open since 1992 when N. Z. Shor asked for the complexity of deciding convexity for quartic polynomials…”
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Semiglobal exponential stability of the discrete-time Arrow-Hurwicz-Uzawa primal-dual algorithm for constrained optimization by Bin, Michelangelo, Notarnicola, Ivano, Parisini, Thomas

“…We consider the discrete-time Arrow-Hurwicz-Uzawa primal-dual algorithm, also known as the first-order Lagrangian method, for constrained optimization problems involving a smooth strongly convex cost…”
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Randomized Methods for Computing Optimal Transport Without Regularization and Their Convergence Analysis by Xie, Yue, Wang, Zhongjian, Zhang, Zhiwen

“…The optimal transport (OT) problem can be reduced to a linear programming (LP) problem through discretization…”
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Whitney differentiability of optimal-value functions for bound-constrained convex programming problems by Khan, Kamil A.

“… This article shows that optimal-value functions for bound-constrained convex programmes with Whitney-differentiable objective functions are themselves Whitney-differentiable, even when the…”
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Convex optimization via inertial algorithms with vanishing Tikhonov regularization: fast convergence to the minimum norm solution by Attouch, Hedy, László, Szilárd Csaba

“…In a Hilbertian framework, for the minimization of a general convex differentiable function f , we introduce new inertial dynamics and algorithms that generate trajectories and iterates that converge…”
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Copositivity and constrained fractional quadratic problems by Amaral, Paula, Bomze, Immanuel M., Júdice, Joaquim

“…) and Standard Fractional Quadratic Problem (StFQP). Based on these formulations, Semidefinite Programming relaxations are derived for finding good lower bounds…”
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Enhanced Computation of the Proximity Operator for Perspective Functions by Briceño-Arias, Luis M., Vivar-Vargas, Cristóbal

“…In this paper, we provide an explicit expression for the proximity operator of a perspective of any proper lower semicontinuous convex function defined on a Hilbert space…”
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Convex optimization for the planted k-disjoint-clique problem by Ames, Brendan P. W., Vavasis, Stephen A.

“…We consider the k -disjoint-clique problem. The input is an undirected graph G in which the nodes represent data items, and edges indicate a similarity between…”
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Differential stability of convex optimization problems under weaker conditions by An, Duong Thi Viet, Köbis, Markus A., Tuyen, Nguyen Van

“…Differential stability properties of convex optimization problems in Hausdorff locally convex topological vector spaces are considered in this paper…”
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