- Referenced in 148 articles
- functions We consider the problem of minimizing a smooth objective function f of n real ... storage and computing time by using a minimization algorithm that exploits some special structure...
- Referenced in 147 articles
- Fortran 77 subroutines for minimizing a smooth function subject to constraints, which may include simple...
- Referenced in 117 articles
- ALGENCAN) is a Fortran code for minimizing a smooth function with a potentially large number...
- Referenced in 106 articles
- NCMND minimizes a smooth nonlinear function of n variables. A subroutine that computes the function...
- Referenced in 631 articles
- optimization methods for unconstrained and bound constrained minimization problems. The style of the book ... pages, is devoted to the optimization of smooth functions. The methods studied in this first ... such cases the noise often introduces artificial minimizers. Gradient information, even if available, cannot expected ... treatment of both, optimization methods for smooth and for noisy functions is a unique feature...
- Referenced in 35 articles
- problem. We also discuss Nesterov’s smooth minimization technique applied to the SDP arising...
- Referenced in 68 articles
- problem, which involves the minimization of a smaller and smooth quadratic function, is solved...
- Referenced in 50 articles
- concerned with minimization or maximization of nonlinear, possibly non-smooth objective functions and solution...
- Referenced in 61 articles
- functions for the minimization of the maximum of a set of smooth objective functions (possibly...
- Referenced in 9 articles
- regularization. The proposed SpicyMKL iteratively solves smooth minimization problems. Thus, there is no need...
- Referenced in 18 articles
- HANSO: Hybrid Algorithm for Non-Smooth Optimization A MATLAB package based on the BFGS ... sampling methods. For general unconstrained minimization: convex or nonconvex, smooth or nonsmooth, including BFGS, limited...
- Referenced in 43 articles
- minimization. They do not need to know in advance the actual level of smoothness...
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- optimization. Augmented Lagrangian Adaptive Barrier Minimization Algorithm for optimizing smooth nonlinear objective functions with constraints...
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- problem of minimizing a continuously differentiable function of several variables subject to smooth nonlinear constraints ... neither calculated nor explicitly approximated. Hence, every minimization procedure must use only a suitable sampling...
- Referenced in 18 articles
- have shown that smooth strongly convex finite sums can be minimized faster than by treating...
- Referenced in 8 articles
- points, with the degree of smoothing chosen to minimize the expected mean square error ... with the data is known, or, to minimize the generalized cross validation (GCV) when ... matrices associated with the cubic smoothing spline problem. Bayesian point error estimates are also calculated...
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- generalize Newton-type methods for minimizing smooth functions to handle a sum of two convex ... functions: a smooth function and a nonsmooth function with a simple proximal mapping. We show ... behavior of Newton-type methods for minimizing smooth functions, even when search directions are computed...
- Referenced in 131 articles
- already quite immense. This paper applies a smoothing technique and an accelerated first-order algorithm ... fewer alternatives, such as total-variation minimization and convex programs seeking to minimize...
- Referenced in 38 articles
- relaxation (with regularization) of the non-convex minimization problem (1), and use the SDP computed ... with backtracking line search to solve the smooth unconstrained problem (2). This software package...
- Referenced in 4 articles
- cluster is obtained by minimizing a smooth kernel function. Although in our applications we have...