<p>This paper focuses on solving the distributed optimization problem with binary-valued intermittent measurements of local objective functions. In this paper, a binary-valued measurement represents whether the measured value is smaller than a fixed threshold. Meanwhile, the “intermittent” scenario arises when there is a non-zero probability of not detecting each local function value during the measuring process. Using this kind of coarse measurement, the authors propose a discrete-time stochastic extremum seeking-based algorithm for distributed optimization over a directed graph. As is well-known, many existing distributed optimization algorithms require a doubly-stochastic weight matrix to ensure the average consensus of agents. However, in practical engineering, achieving double-stochasticity, especially for directed graphs, is not always feasible or desirable. To overcome this limitation, the authors design a row-stochastic matrix and a column-stochastic matrix as weight matrices in the proposed algorithm instead of relying on doubly-stochasticity. Under some mild conditions, the authors rigorously prove that agents can reach the average consensus and ultimately find the optimal solution. Finally, the authors provide a numerical example to illustrate the effectiveness of the algorithm.</p>