Because of the existence of specific object intensity distributions, the inverse problem of hologram synthesis in CGH can also be cast as the optimization of a parameterized objective function requiring minimiz
OPTIMIZATIONCONVEXINVERSEComputer-generated holography is a promising technique that modulates user-defined wavefronts with digital holograms. Computing appropriate holograms with faithful reconstructions is not only a problem closely related to the fundamental basis of holography but also a long-standing ...
Optimal Black-Box Reductions Between Optimization Objectives By Zeyuan Allen-Zhu and Elad Hazan 2016a, AllenZhu-Hazan : non-convex SVRG Variance Reduction for Faster Non-Convex Optimization By Zeyuan Allen-Zhu and Elad Hazan 2016, Wibisono-Wilson-Jordan, A Variational Perspective on Accelerated Method...
Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci., 2(1):183–202, 2009. [2] J.M. Borwein and A.S. Lewis. Convex analysis and nonlinear optimization. Springer, 2006. [3] L. Bottou. Online algorithms and stochastic approximations....
1.1.1Inverse problems with non-convex regularization An important instance of (Opt) is the composite optimization problem (1) whereis a smooth data fidelity function,is a convex function,, andis a regularization parameter. A common use of this problem formulation is the regularized empirical risk...
Furthermore, the presented network is also capable of solving convex or nonconvex real-variable optimization problem (RVNOP). Different from other existing neural networks for RVNOP, our network avoids the redundant computation of inverse matrix and relaxes some additional assumptions, comprising the ...
The proximity operator is a generalized form of the projection operator, often used to solve non-differentiable optimization problems. In this paper, we use it to solve the nonconvex subproblems in the iterative algorithm. For a proper and lower semi-continuous functionP(x), the corresponding pro...
The above methods represent convex optimization methods for theℓ1-constrained convex CDL problem. Although one primary advantage of the convex optimization methods is that they circumvent some computational burden than the nonconvex optimization methods, they may have non-sparse representations of coeffi...
Many tasks in real life scenarios can be naturally formulated as nonconvex optimization problems. Unfortunately, to date, the iterative numerical methods t
Abstract We propose a Bregman inertial forward-reflected-backward (BiFRB) method for nonconvex composite problems. Assuming the generalized concave Kurdyka-Łojasiewicz property, we obtain sequential convergence of BiFRB, as well as convergence rates on both the function value and actual sequence. One...