Results 71 to 80 of about 7,288,186 (192)
Global solutions to nonconvex optimization of 4th-order polynomial and log-sum-exp functions [PDF]
, 2016 This paper presents a canonical dual approach for solving a nonconvex global optimization problem governed by a sum of 4th-order polynomial and a log-sum-exp function. Such a problem arises extensively in engineering and sciences.
Chen, Yi, Gao, David
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Certified Global Optimality for Nonconvex Integer Programs via Extended Formulations
Nonconvex integer programs (NCIPs) pose significant challenges in optimization due to the inherent difficulties arising from both nonconvexity and integrality constraints. Finding globally optimal solutions for such problems is often computationally intractable, and even establishing bounds on the global optimum can be highly complex.
Revista, Zen, MATH, 10
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Fractal and FractionalEconomic dispatch in wind-integrated power systems is a critical challenge, yet many recent metaheuristics suffer from premature convergence, heavy parameter tuning, and limited ability to escape local optima in non-smooth valve-point landscapes.
Abdul Wadood +4 more
doaj +1 more source
Immunizing Conic Quadratic Optimization Problems Against Implementation Errors [PDF]
We show that the robust counterpart of a convex quadratic constraint with ellipsoidal implementation error is equivalent to a system of conic quadratic constraints.
Ben-Tal, A., Hertog, D. den
core
Generalized S-Lemma and strong duality in nonconvex quadratic programming
, 2013 On the basis of a new topological minimax theorem, a simple and unified approach is developed to Lagrange duality in nonconvex quadratic programming.
Tuy, H, Tuan, HD
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IEEE AccessThe global drive towards carbon neutrality has led to a significant increase in the number of power plants based on renewable energy sources (RES). Concurrently, numerous households are adopting RES to generate their own energy, aiming to decrease both ...
A. Daniel Carnerero +7 more
doaj +1 more source
Breaking the Nonconvexity Barrier: Certified Global Optimality in Mixed-Integer Programming
Mixed-Integer Programming (MIP) is a powerful framework for modeling real-world optimization problems that involve both continuous and discrete decision variables. While convex MIPs are largely tractable using sophisticated branch-and-cut algorithms, the presence of nonconvexity in the objective function or constraints introduces significant ...
Revista, Zen, MATH, 10
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Why Methods for Optimization Problems with Time-Consuming Function Evaluations and Integer Variables Should Use Global Approximation Models [PDF]
This paper advocates the use of methods based on global approximation models for optimization problems with time-consuming function evaluations and integer variables.We show that methods based on local approximations may lead to the integer rounding of ...
Brekelmans, R.C.M. +4 more
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Certifiable Global Optimality for Large-Scale Nonconvex Mixed-Integer Programs
Mixed-Integer Nonlinear Programming (MINLP) problems are ubiquitous in science and engineering, modeling complex systems where decisions are both continuous and discrete, and relationships are nonlinear. However, the presence of nonconvexity and large-scale structures presents significant challenges to obtaining and, crucially, certifying global ...
Revista, Zen, MATH, 10
openaire +2 more sources
Applications and enhancements of aircraft design optimization techniques
, 2012 The aircraft industry has been at the forefront in developing design optimization strategies ever since the advent of high performance computing. Thanks to the large computational resources now available, many new as well as more mature optimization ...
Powell, Stephen
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