A normal fan projection algorithm for low-rank optimization.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 681, doi. 10.1007/s10107-024-02079-y
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Works matching IS 00255610 AND DT 2025 AND VI 209 AND IP 1
Results: 20
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Sample complexity analysis for adaptive optimization algorithms with stochastic oracles.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 651, doi. 10.1007/s10107-024-02078-z
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- Article
3
An asynchronous proximal bundle method: An asynchronous proximal bundle method: F. Fischer.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 825, doi. 10.1007/s10107-024-02088-x
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4
Finding global minima via kernel approximations: Finding global minima via kernel approximations: A. Rudi et al.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 703, doi. 10.1007/s10107-024-02081-4
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5
Perseus: a simple and optimal high-order method for variational inequalities: Perseus: a simple and optimal high-order method for...: T. Lin, M. I. Jordan.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 609, doi. 10.1007/s10107-024-02075-2
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6
On the directional asymptotic approach in optimization theory: The directional asymptotic approach: M. Benko, P. Mehlitz.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 859, doi. 10.1007/s10107-024-02089-w
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7
Non-convex scenario optimization.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 557, doi. 10.1007/s10107-024-02074-3
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8
Stackelberg risk preference design.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 785, doi. 10.1007/s10107-024-02083-2
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9
Accelerated first-order methods for a class of semidefinite programs.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 503, doi. 10.1007/s10107-024-02073-4
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- Article
10
Sum-of-squares relaxations for polynomial min–max problems over simple sets.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 475, doi. 10.1007/s10107-024-02072-5
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- Article
11
Convergence rates for sums-of-squares hierarchies with correlative sparsity.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 435, doi. 10.1007/s10107-024-02071-6
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12
Level constrained first order methods for function constrained optimization.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 1, doi. 10.1007/s10107-024-02057-4
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- Article
13
Polyhedral properties of RLT relaxations of nonconvex quadratic programs and their implications on exact relaxations: Polyhedral properties of RLT relaxations of nonconvex quadratic...: Y. Qiu, E. A. Yıldırım.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 397, doi. 10.1007/s10107-024-02070-7
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14
The Chvátal–Gomory procedure for integer SDPs with applications in combinatorial optimization.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 323, doi. 10.1007/s10107-024-02069-0
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- Article
15
Generalized minimum 0-extension problem and discrete convexity: Generalized minimum 0-extension problem and discrete...: M. Dvorak, V. Kolmogorov.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 279, doi. 10.1007/s10107-024-02064-5
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16
Generalized Nash equilibrium problems with mixed-integer variables.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 231, doi. 10.1007/s10107-024-02063-6
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- Article
17
Hessian barrier algorithms for non-convex conic optimization: Hessian barrier algorithms for non-convex conic...: P. Dvurechensky, M. Staudigl.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 171, doi. 10.1007/s10107-024-02062-7
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18
Automated tight Lyapunov analysis for first-order methods: Automated tight Lyapunov analysis...: M. Upadhyaya et al.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 133, doi. 10.1007/s10107-024-02061-8
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19
The effect of smooth parametrizations on nonconvex optimization landscapes.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 63, doi. 10.1007/s10107-024-02058-3
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20
Convex hulls of monomial curves, and a sparse positivstellensatz: Convex hulls of monomial curves, and a...: G. Averkov, C. Scheiderer.
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- Mathematical Programming, 2025, v. 209, n. 1, p. 113, doi. 10.1007/s10107-024-02060-9
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