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This work studies multi-agent sharing optimization problems with the objective function being the sum of smooth local functions plus a convex (possibly non-smooth) function coupling all agents. This scenario arises in many machine learning and engineering ...
We consider the problem of finding a saddle point for the convex-concave objective minxmaxyf(x)+⟨Ax,y⟩−g∗(y), where f is a convex function with locally Lipschitz gradient and g is convex and possibly non-smooth. We propose an ...
We introduce a new primal-dual reconstruction algorithm for fluorescence and bioluminescence tomography. As often in optical tomography, image reconstruction is performed by optimizing a multi-term convex cost function. Current reconstruction methods emplo ...
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We introduce a new primal-dual reconstruction algorithm for fluorescence and bioluminescence tomography. As often in optical tomography, image reconstruction is performed by optimizing a multi-term convex cost function. Current reconstruction methods emplo ...
