Výsledky vyhľadávania - 90C25 Mathematical Programming - convex programming

On semidefinite programming relaxations for a class of robust SOS-convex polynomial optimization problems Autor 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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Shapes and recession cones in mixed-integer convex representability Autor 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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Data-driven inverse optimization with imperfect information Autor 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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Convex hull results on quadratic programs with non-intersecting constraints Autor 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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A slightly lifted convex relaxation for nonconvex quadratic programming with ball constraints Autor 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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A Benson-type algorithm for bounded convex vector optimization problems with vertex selection Autor Dörfler, Daniel, Löhne, Andreas, Schneider, Christopher, Weißing, Benjamin

“…We present an algorithm for approximately solving bounded convex vector optimization problems…”
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New analysis and results for the Frank–Wolfe method Autor 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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Complexity of first-order inexact Lagrangian and penalty methods for conic convex programming Autor 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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NP-hardness of deciding convexity of quartic polynomials and related problems Autor 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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Copositivity and constrained fractional quadratic problems Autor 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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Semiglobal exponential stability of the discrete-time Arrow-Hurwicz-Uzawa primal-dual algorithm for constrained optimization Autor 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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A unified approach to error bounds for structured convex optimization problems Autor Zhou, Zirui, So, Anthony Man-Cho

“… convex optimization problems, in which the objective function is the sum of a smooth convex function…”
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Branch-and-bound performance estimation programming: a unified methodology for constructing optimal optimization methods Autor 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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Mathematical programming with Semilocally Subconvex functions over cones Autor 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 Autor 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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On the convex hull of convex quadratic optimization problems with indicators Autor Wei, Linchuan, Atamtürk, Alper, Gómez, Andrés, Küçükyavuz, Simge

“…We consider the convex quadratic optimization problem in R n with indicator variables and arbitrary constraints on the indicators…”
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Randomized Methods for Computing Optimal Transport Without Regularization and Their Convergence Analysis Autor 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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FISTA is an automatic geometrically optimized algorithm for strongly convex functions Autor Aujol, J.-F., Dossal, Ch, Rondepierre, A.

“…In this work, we are interested in the famous FISTA algorithm. We show that FISTA is an automatic geometrically optimized algorithm for functions satisfying a…”
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Convex optimization via inertial algorithms with vanishing Tikhonov regularization: fast convergence to the minimum norm solution Autor 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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Differential stability of convex optimization problems under weaker conditions Autor 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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